fix(build): refresh dependency locks after JMH classpath isolation

* cover all Radixor dictionary languages
* add PRIMARY_OUTPUT, ANY_CANDIDATE, and ALL_CANDIDATES policies
* measure pairwise over-stemming and under-stemming
* add balanced accuracy and complementary quality metrics
* compare single-output and multi-output stemmers fairly
* improve result validation, reporting, and documentation
* move stemming quality tests into the standard test source set
* preserve the existing JMH benchmark structure and badge output

.github/workflows/benchmarks.yml

@@ -56,7 +56,7 @@ jobs:
           test -f gradle/verification-metadata.xml
 
       - name: Run JMH benchmarks
-        run: ./gradlew clean jmh -Pjmh.includes='.*StemmerComparisonBenchmark.*' --no-daemon
+        run: ./gradlew clean jmh -Pjmh.includes='.*EnglishStemmerComparisonBenchmark.*' --no-daemon
 
       - name: Upload JMH reports
         uses: actions/upload-artifact@v4

.github/workflows/pages.yml

@@ -70,7 +70,7 @@ jobs:
           test -f gradle/verification-metadata.xml
 
       - name: Build reports for publication
-        run: ./gradlew --no-daemon clean ciRelease pmdMain javadoc jacocoCiReleaseReport pitest jmh -Pjmh.includes='.*StemmerComparisonBenchmark.*' cyclonedxBom
+        run: ./gradlew --no-daemon clean ciRelease pmdMain javadoc jacocoCiReleaseReport pitest jmh -Pjmh.includes='.*EnglishStemmerComparisonBenchmark.*' cyclonedxBom
 
       - name: Prepare gh-pages worktree
         shell: bash
@@ -275,11 +275,26 @@ jobs:
           - [Browse historical build reports](https://leogalambos.github.io/Radixor/builds/)
           EOF
 
+          # Retain only the 10 most recent numbered builds to stay within
+          # GitHub Pages capacity limits. The "latest" alias is kept separately.
+          mapfile -t EXPIRED_BUILDS < <(
+            find "${SITE_DIR}/builds" -mindepth 1 -maxdepth 1 -type d -printf '%P\n' \
+              | grep -E '^[0-9]+$' \
+              | sort -r -n \
+              | tail -n +11
+          )
+
+          for build in "${EXPIRED_BUILDS[@]}"; do
+            rm -rf "${SITE_DIR}/builds/${build}"
+          done
+
           {
             echo "# Historical Build Reports"
             echo
             echo "The following build report sets are currently published on GitHub Pages."
             echo
+            echo "To stay within GitHub Pages capacity limits, only the 10 most recent build report sets are retained."
+            echo
             echo "| Build | Published | Link |"
             echo "|---:|---|---|"
 

.gitignore

@@ -37,6 +37,7 @@ local.properties
 .settings/
 .loadpath
 .recommenders
+.classpath
 
 # External tool builders
 .externalToolBuilders/

build.gradle

@@ -30,6 +30,11 @@ apply from: 'gradle/maven-pom.gradle'
 
 configurations {
     mockitoAgent
+    stemmingQualityJmhRuntime {
+        canBeConsumed = false
+        canBeResolved = true
+        extendsFrom(jmhImplementation, jmhRuntimeOnly)
+    }
 }
 
 java {
@@ -78,6 +83,16 @@ dependencies {
     }
 }
 
+sourceSets.jmh.compileClasspath = sourceSets.jmh.compileClasspath - sourceSets.test.output
+sourceSets.jmh.runtimeClasspath = sourceSets.jmh.runtimeClasspath - sourceSets.test.output
+sourceSets.test.compileClasspath += sourceSets.jmh.output + configurations.jmhCompileClasspath
+sourceSets.test.runtimeClasspath += sourceSets.jmh.output + configurations.jmhCompileClasspath
+
+tasks.named('compileJmhJava', JavaCompile) {
+    classpath = classpath - sourceSets.test.output
+    setDependsOn([tasks.named('classes')])
+}
+
 dependencyCheck {
     failBuildOnCVSS = 7.0
     failOnError = true
@@ -426,6 +441,7 @@ tasks.named('distTar') {
 
 jmh {
     jmhVersion = '1.37'
+    includeTests = false
     warmupIterations = 3
     iterations = 5
     fork = 1
@@ -442,7 +458,7 @@ jmh {
 
 tasks.named('jmh') {
     group = 'verification'
-    description = 'Runs JMH benchmarks for the Radixor algorithmic core and Snowball comparison suite.'
+    description = 'Runs JMH benchmarks for the Radixor algorithmic core and external stemmer comparison suites.'
 }
 
 apply from: 'gradle/lucene-benchmarks.gradle'
@@ -468,6 +484,85 @@ tasks.register('regressionArtifactGenerator', JavaExec) {
     }
 }
 
+tasks.register('stemmingQuality', JavaExec) {
+    group = 'verification'
+    description = 'Evaluates pairwise over-stemming and under-stemming against bundled dictionary groups.'
+    dependsOn(tasks.named('testClasses'))
+    dependsOn(tasks.named('jmhClasses'))
+    classpath = files(sourceSets.test.runtimeClasspath, configurations.stemmingQualityJmhRuntime)
+    mainClass = 'org.egothor.stemmer.benchmark.quality.StemmingQualityApplication'
+    args layout.buildDirectory.dir('reports/stemming-quality').get().asFile.absolutePath,
+            layout.projectDirectory.dir('src/main/resources').asFile.absolutePath,
+            providers.gradleProperty('stemmingQualityLanguage').getOrElse(''),
+            providers.gradleProperty('stemmingQualityStemmer').getOrElse(''),
+            providers.gradleProperty('stemmingQualityMode').getOrElse(''),
+            providers.gradleProperty('stemmingQualityOutputPolicy').getOrElse(''),
+            providers.gradleProperty('stemmingQualityRankMetric').getOrElse('PAIRWISE_F05'),
+            providers.gradleProperty('stemmingQualityAudit').getOrElse('false'),
+            providers.gradleProperty('stemmingQualityAuditLimit').getOrElse('25')
+    maxHeapSize = '6g'
+}
+
+tasks.register('publishStemmingQualityDocumentation', JavaExec) {
+    group = 'documentation'
+    description = 'Publishes validated complete stemming-quality results on the language benchmark pages.'
+    dependsOn(tasks.named('testClasses'))
+    classpath = sourceSets.test.runtimeClasspath
+    mainClass = 'org.egothor.stemmer.benchmark.quality.StemmingQualityDocumentationPublisher'
+    args layout.buildDirectory.file('reports/stemming-quality/stemming-quality.csv').get().asFile.absolutePath,
+            layout.projectDirectory.dir('docs').asFile.absolutePath,
+            'update'
+    doFirst {
+        if (!file("$buildDir/reports/stemming-quality/stemming-quality.csv").isFile()) {
+            throw new GradleException('A complete stemming-quality CSV is required. Run stemmingQuality only when no validated complete report is available.')
+        }
+    }
+}
+
+tasks.register('verifyStemmingQualityDocumentation', JavaExec) {
+    group = 'verification'
+    description = 'Verifies published language-page quality tables against the checked-in authoritative CSV.'
+    dependsOn(tasks.named('testClasses'))
+    classpath = sourceSets.test.runtimeClasspath
+    mainClass = 'org.egothor.stemmer.benchmark.quality.StemmingQualityDocumentationPublisher'
+    args layout.projectDirectory.file('docs/benchmarks/data/stemming-quality.csv').asFile.absolutePath,
+            layout.projectDirectory.dir('docs').asFile.absolutePath,
+            'verify'
+}
+
+tasks.named('check') {
+    dependsOn(tasks.named('verifyStemmingQualityDocumentation'))
+}
+
+tasks.register('verifyStemmingQualitySourceSets') {
+    group = 'verification'
+    description = 'Verifies the production, JMH, and standard-test ownership of stemming-quality infrastructure.'
+    doLast {
+        if (sourceSets.findByName('stemmingQualityTest') != null || file('src/stemmingQualityTest').exists()) {
+            throw new GradleException('The obsolete stemmingQualityTest source set or directory still exists.')
+        }
+        if (!file('src/jmh/java/org/egothor/stemmer/benchmark/QualityStemmerMatrix.java').isFile()) {
+            throw new GradleException('The authoritative JMH stemmer matrix is not in src/jmh.')
+        }
+        if (!file('src/test/java/org/egothor/stemmer/benchmark/quality/StemmingQualityApplication.java').isFile()) {
+            throw new GradleException('The stemming-quality evaluator is not in the standard test source set.')
+        }
+    }
+}
+
+tasks.register('verifyProductionJarExcludesStemmingQuality') {
+    group = 'verification'
+    description = 'Verifies that analytical stemming-quality classes are absent from the production JAR.'
+    dependsOn(tasks.named('jar'))
+    doLast {
+        final File archive = tasks.named('jar').get().archiveFile.get().asFile
+        final def forbidden = zipTree(archive).matching { include '**/benchmark/**' }.files
+        if (!forbidden.isEmpty()) {
+            throw new GradleException("Production JAR contains analytical stemming-quality classes: ${forbidden}")
+        }
+    }
+}
+
 tasks.register('printDependencyCheckNvdConfig') {
     doLast {
         System.out.println("NVD API key present: " + (nvdApiKey != null && !nvdApiKey.isBlank()))
@@ -518,6 +613,8 @@ javadoc {
 apply from: 'gradle/snowball-benchmarks.gradle'
 apply from: 'gradle/paicehusk-benchmarks.gradle'
 apply from: 'gradle/opennlp-benchmarks.gradle'
+apply from: 'gradle/hunspell-benchmarks.gradle'
+apply from: 'gradle/cistem-benchmarks.gradle'
 
 gradle.taskGraph.whenReady { taskGraph ->
     def banner = """

docs/assets/stylesheets/extra.css

@@ -67,6 +67,104 @@
   padding: 0.45rem 0.7rem;
 }
 
+/* Publication-quality benchmark tables retain identity columns while scrolling. */
+.quality-table {
+  max-width: 100%;
+  overflow-x: auto;
+  margin: 0.65rem 0 1rem;
+  border: 1px solid var(--md-default-fg-color--lightest);
+  border-radius: 0.2rem;
+  scrollbar-gutter: stable;
+}
+
+.quality-table:focus {
+  outline: 0.15rem solid var(--md-accent-fg-color);
+  outline-offset: 0.1rem;
+}
+
+.quality-table::before {
+  content: "Scrollable table: Rank, Stemmer, and Output policy remain visible.";
+  display: block;
+  padding: 0.35rem 0.55rem;
+  color: var(--md-default-fg-color--light);
+  font-size: 0.68rem;
+}
+
+.quality-table .md-typeset__table,
+.quality-table table {
+  margin: 0;
+}
+
+.quality-table table th:nth-child(1),
+.quality-table table td:nth-child(1),
+.quality-table table th:nth-child(2),
+.quality-table table td:nth-child(2),
+.quality-table table th:nth-child(3),
+.quality-table table td:nth-child(3) {
+  position: sticky;
+  z-index: 2;
+  background: var(--md-default-bg-color);
+  background-clip: padding-box;
+}
+
+.quality-table table th:nth-child(1),
+.quality-table table td:nth-child(1) {
+  left: 0;
+  min-width: 2.8rem;
+}
+
+.quality-table table th:nth-child(2),
+.quality-table table td:nth-child(2) {
+  left: 2.8rem;
+  min-width: 13rem;
+  white-space: normal;
+}
+
+.quality-table table th:nth-child(3),
+.quality-table table td:nth-child(3) {
+  left: 15.8rem;
+  min-width: 8.5rem;
+  box-shadow: 0.2rem 0 0.25rem rgb(0 0 0 / 8%);
+}
+
+.quality-details > summary {
+  font-weight: 600;
+}
+
+@media screen and (max-width: 44.99em) {
+  .quality-table table th:nth-child(2),
+  .quality-table table td:nth-child(2) {
+    min-width: 10rem;
+  }
+
+  .quality-table table th:nth-child(3),
+  .quality-table table td:nth-child(3) {
+    position: static;
+    min-width: 7.5rem;
+    box-shadow: none;
+  }
+}
+
+@media print {
+  .quality-table {
+    overflow: visible;
+    border: 0;
+  }
+
+  .quality-table::before {
+    display: none;
+  }
+
+  .quality-table table th,
+  .quality-table table td {
+    position: static !important;
+  }
+
+  .quality-details:not([open]) > *:not(summary) {
+    display: block;
+  }
+}
+
 /* Code blocks */
 .md-typeset pre > code {
   font-size: 0.72rem;

docs/benchmarking.md

@@ -10,7 +10,7 @@ This page is the entry point for benchmark interpretation. Detailed tables and l
 - Radixor is the quality-oriented baseline in same-language comparisons. Its exact-root accuracy is often close to 100%, while many faster competitors are light, minimal, possessive, or aggressive rule-based stemmers with much lower root agreement.
 - The measured Radixor cost buys dictionary-trained stemming precision. That precision improves search quality by mapping inflected forms to intended dictionary roots instead of approximate or over-reduced stems.
 - Speed benchmarks process changed dictionary tokens where the surface form differs from the expected root. Accuracy benchmarks process the complete dictionary.
-- Accuracy-only benchmarks intentionally use one deterministic JMH measurement iteration without warmup because repeated precision passes would duplicate the same counters.
+- Accuracy tables use deterministic auxiliary counters from the current JMH reports. Repeated measurement samples duplicate the same exact-root accounting and are not interpreted as timing results.
 - The historical Porter performance badge is retired. Benchmark reporting now uses speed and quality tables rather than a single Porter ratio.
 
 ## Benchmark Documentation Map
@@ -18,6 +18,9 @@ This page is the entry point for benchmark interpretation. Detailed tables and l
 | Page | Purpose |
 | --- | --- |
 | [Benchmark methodology](benchmarks/reference/methodology.md) | Workload design, speed pass, quality pass, normalization policy, and exact-root metrics. |
+| [Linguistic quality methodology](benchmarks/reference/linguistic-quality.md) | Pairwise gold standard, over/under-stemming, candidate policies, metrics, and ranking rules. |
+| [Tested stemmers](benchmarks/reference/tested-stemmers.md) | Upstream attribution, tested versions, language coverage, adapter behaviour, and limitations. |
+| [Reproducibility and raw data](benchmarks/reference/reproducibility.md) | Versioned quality snapshot, checksum, commands, reports, and provenance limitations. |
 | [Benchmark corpora](benchmarks/reference/corpora.md) | Dictionary row counts, complete quality tokens, already-root tokens, changed speed tokens, and timing token counts. |
 | [Benchmark environment and reports](benchmarks/reference/environment.md) | Hardware, OS, JVM, JMH settings, report files, and current badge/report policy. |
 | [English dictionary coverage benchmark](benchmarks/reference/english-coverage.md) | The quality/speed operating curve for contracted Radixor tries built from 100% down to 10% of English dictionary rows. |

docs/benchmarks/data/stemming-quality.csv

@@ -0,0 +1,309 @@
+Stemmer,Language,Dictionary mode,Output policy,Applied dictionary rows,Processed word forms,Singleton dictionary rows,Forms with one candidate,Forms with multiple candidates,Maximum candidates for one form,Total candidate assignments,Distinct output stems,True-positive pairs,False-positive pairs,False-negative pairs,True-negative pairs,Over-stemming error pairs,Over-stemming possible pairs,Over-stemming percentage,Under-stemming error pairs,Under-stemming possible pairs,Under-stemming percentage,Pairwise precision,Pairwise recall,Pairwise specificity,Pairwise accuracy,Balanced accuracy,Pairwise F0.5,Pairwise F1,Pairwise F2,Jaccard index,Fowlkes-Mallows index,Matthews correlation coefficient,Pairwise error rate,Adjusted Rand Index,Homogeneity,Completeness,V-measure,Normalized mutual information
+"CZECH_LUCENE_CZECH_STEM_FILTER","CS_CZ","ALL_WORDS","PRIMARY_OUTPUT","5113","51676","2","51676","0","1","51676","9647","177249","14480","124586","1334862335","14480","1334876815","0.001085","124586","301835","41.276194","0.924476735392","0.587238060530","0.999989152557","0.999895844650","0.793613606543","0.829234311828","0.718241200736","0.633453389361","0.560355974266","0.736809286788","0.736765291417","0.000104155350","0.718191706079","0.993800637348","0.944976928457","0.968774025802","0.968774025802"
+"CZECH_LUCENE_CZECH_STEM_FILTER","CS_CZ","LOWERCASE_GROUPS_ONLY","PRIMARY_OUTPUT","5038","50968","2","50968","0","1","50968","9558","174387","13950","124426","1298530265","13950","1298544215","0.001074","124426","298813","41.640089","0.925930645598","0.583599107134","0.999989257201","0.999893462107","0.791794182167","0.828708724235","0.715947860002","0.630197985095","0.557569149804","0.735100195918","0.735055396892","0.000106537893","0.715897321649","0.993897397445","0.944297456221","0.968462775946","0.968462775946"
+"CZECH_RADIXOR","CS_CZ","ALL_WORDS","PRIMARY_OUTPUT","5113","51676","2","51676","0","1","51676","5162","299762","3867","2073","1334872948","3867","1334876815","0.000290","2073","301835","0.686799","0.987264062392","0.993132009210","0.999997103103","0.999995551157","0.996564556157","0.988432097845","0.990189342389","0.991952846154","0.980569312599","0.990193689085","0.990191466141","0.000004448843","0.990187117482","0.998733220675","0.998685552738","0.998709386137","0.998709386137"
+"CZECH_RADIXOR","CS_CZ","ALL_WORDS","ANY_CANDIDATE","5113","51676","2","51080","596","4","52319","5166","301835","0","0","1334876815","0","1334876815","0.000000","0","301835","0.000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","0.000000000000","","","","",""
+"CZECH_RADIXOR","CS_CZ","ALL_WORDS","ALL_CANDIDATES","5113","51676","2","51080","596","4","52319","5166","301835","5850","0","1334870965","5850","1334876815","0.000438","0","301835","0.000000","0.980987048442","1.000000000000","0.999995617573","0.999995618564","0.999997808787","0.984731579205","0.990402283764","0.996138677580","0.980987048442","0.990447902942","0.990445732657","0.000004381436","","","","",""
+"CZECH_RADIXOR","CS_CZ","LOWERCASE_GROUPS_ONLY","PRIMARY_OUTPUT","5038","50968","2","50968","0","1","50968","5037","297104","3863","1709","1298540352","3863","1298544215","0.000297","1709","298813","0.571930","0.987164705765","0.994280703985","0.999997025130","0.999995710028","0.997138864558","0.988579745136","0.990709926973","0.992849308824","0.981590876052","0.990716315904","0.990714173387","0.000004289972","0.990707781520","0.998726091764","0.999029907266","0.998877976413","0.998877976413"
+"CZECH_RADIXOR","CS_CZ","LOWERCASE_GROUPS_ONLY","ANY_CANDIDATE","5038","50968","2","50428","540","4","51543","5040","298813","0","0","1298544215","0","1298544215","0.000000","0","298813","0.000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","0.000000000000","","","","",""
+"CZECH_RADIXOR","CS_CZ","LOWERCASE_GROUPS_ONLY","ALL_CANDIDATES","5038","50968","2","50428","540","4","51543","5040","298813","5782","0","1298538433","5782","1298544215","0.000445","0","298813","0.000000","0.981017416570","1.000000000000","0.999995547321","0.999995548346","0.999997773661","0.984756059381","0.990417760454","0.996144940117","0.981017416570","0.990463233325","0.990461028216","0.000004451654","","","","",""
+"DA_DK_RADIXOR","DA_DK","ALL_WORDS","PRIMARY_OUTPUT","4179","28079","32","28079","0","1","28079","4184","89188","1165","707","394110021","1165","394111186","0.000296","707","89895","0.786473","0.987106128186","0.992135268925","0.999997043981","0.999995251155","0.996066156453","0.988107873355","0.989614309174","0.991125345329","0.979442126071","0.989617503860","0.989615130363","0.000004748845","0.989611934224","0.998465862775","0.998718664384","0.998592247580","0.998592247580"
+"DA_DK_RADIXOR","DA_DK","ALL_WORDS","ANY_CANDIDATE","4179","28079","32","27756","323","3","28405","4187","89895","0","0","394111186","0","394111186","0.000000","0","89895","0.000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","0.000000000000","","","","",""
+"DA_DK_RADIXOR","DA_DK","ALL_WORDS","ALL_CANDIDATES","4179","28079","32","27756","323","3","28405","4187","89895","1849","0","394109337","1849","394111186","0.000469","0","89895","0.000000","0.979846093478","1.000000000000","0.999995308431","0.999995309500","0.999997654215","0.983811622975","0.989820468071","0.995903164910","0.979846093478","0.989871756076","0.989869434047","0.000004690500","","","","",""
+"DA_DK_RADIXOR","DA_DK","LOWERCASE_GROUPS_ONLY","PRIMARY_OUTPUT","4173","28033","32","28033","0","1","28033","4170","89077","1165","663","392819623","1165","392820788","0.000297","663","89740","0.738801","0.987090268389","0.992611990194","0.999997034271","0.999995347541","0.996304512232","0.988189692661","0.989843428787","0.991502709249","0.979891095099","0.989847279032","0.989844954043","0.000004652459","0.989841102043","0.998463063294","0.998811876590","0.998637439483","0.998637439483"
+"DA_DK_RADIXOR","DA_DK","LOWERCASE_GROUPS_ONLY","ANY_CANDIDATE","4173","28033","32","27718","315","3","28351","4173","89740","0","0","392820788","0","392820788","0.000000","0","89740","0.000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","0.000000000000","","","","",""
+"DA_DK_RADIXOR","DA_DK","LOWERCASE_GROUPS_ONLY","ALL_CANDIDATES","4173","28033","32","27718","315","3","28351","4173","89740","1849","0","392818939","1849","392820788","0.000471","0","89740","0.000000","0.979811986156","1.000000000000","0.999995293019","0.999995294094","0.999997646509","0.983784115625","0.989803065147","0.995896117847","0.979811986156","0.989854527774","0.989852198158","0.000004705906","","","","",""
+"ENGLISH_LUCENE_KSTEM_FILTER","US_UK","ALL_WORDS","PRIMARY_OUTPUT","396939","607439","250964","607439","0","1","607439","371125","237565","1368501","76305","184489083270","1368501","184490451771","0.000742","76305","313870","24.311020","0.147917333410","0.756889795138","0.999992582267","0.999992168681","0.878441188702","0.176283968232","0.247471790726","0.415099040868","0.141208449266","0.334599940499","0.334597833111","0.000007831319","0.247469648794","0.980686838187","0.992107972963","0.986364345289","0.986364345289"
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+"UKRAINIAN_RADIXOR","UK_UA","ALL_WORDS","ALL_CANDIDATES","1493","14245","4","14055","190","2","14435","1493","65340","1490","0","101386060","1490","101387550","0.001470","0","65340","0.000000","0.977704623672","1.000000000000","0.999985303916","0.999985313380","0.999992651958","0.982083809295","0.988726639933","0.995459946982","0.977704623672","0.988789473888","0.988782208195","0.000014686620","","","","",""
+"UKRAINIAN_RADIXOR","UK_UA","LOWERCASE_GROUPS_ONLY","PRIMARY_OUTPUT","1491","14236","4","14236","0","1","14236","1491","64716","880","608","101258526","880","101259406","0.000869","608","65324","0.930745","0.986584547838","0.990692547915","0.999991309449","0.999985314543","0.995341928682","0.987403420118","0.988634280477","0.989868213355","0.977524016676","0.988636414174","0.988629069474","0.000014685457","0.988626933033","0.997992012551","0.998264347489","0.998128161444","0.998128161444"
+"UKRAINIAN_RADIXOR","UK_UA","LOWERCASE_GROUPS_ONLY","ANY_CANDIDATE","1491","14236","4","14046","190","2","14426","1491","65324","0","0","101259406","0","101259406","0.000000","0","65324","0.000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","0.000000000000","","","","",""
+"UKRAINIAN_RADIXOR","UK_UA","LOWERCASE_GROUPS_ONLY","ALL_CANDIDATES","1491","14236","4","14046","190","2","14426","1491","65324","1490","0","101257916","1490","101259406","0.001471","0","65324","0.000000","0.977699284581","1.000000000000","0.999985285318","0.999985294804","0.999992642659","0.982079499669","0.988723909852","0.995458840023","0.977699284581","0.988786774073","0.988779499204","0.000014705196","","","","",""
+"YI_RADIXOR","YI","ALL_WORDS","PRIMARY_OUTPUT","802","3578","0","3578","0","1","3578","802","6195","195","149","6392714","195","6392909","0.003050","149","6344","2.348676","0.969483568075","0.976513240858","0.999969497454","0.999946243726","0.988241369156","0.970881394183","0.972985707555","0.975099162627","0.947392567671","0.972992055990","0.972965163911","0.000053756274","0.972958803000","0.995691103897","0.996142223728","0.995916612726","0.995916612726"
+"YI_RADIXOR","YI","ALL_WORDS","ANY_CANDIDATE","802","3578","0","3489","89","3","3676","802","6344","0","0","6392909","0","6392909","0.000000","0","6344","0.000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","0.000000000000","","","","",""
+"YI_RADIXOR","YI","ALL_WORDS","ALL_CANDIDATES","802","3578","0","3489","89","3","3676","802","6344","389","0","6392520","389","6392909","0.006085","0","6344","0.000000","0.942224862617","1.000000000000","0.999939151332","0.999939211655","0.999969575666","0.953239572064","0.970253116158","0.987885016662","0.942224862617","0.970682678643","0.970653145819","0.000060788345","","","","",""
+"YI_RADIXOR","YI","LOWERCASE_GROUPS_ONLY","PRIMARY_OUTPUT","802","3578","0","3578","0","1","3578","802","6195","195","149","6392714","195","6392909","0.003050","149","6344","2.348676","0.969483568075","0.976513240858","0.999969497454","0.999946243726","0.988241369156","0.970881394183","0.972985707555","0.975099162627","0.947392567671","0.972992055990","0.972965163911","0.000053756274","0.972958803000","0.995691103897","0.996142223728","0.995916612726","0.995916612726"
+"YI_RADIXOR","YI","LOWERCASE_GROUPS_ONLY","ANY_CANDIDATE","802","3578","0","3489","89","3","3676","802","6344","0","0","6392909","0","6392909","0.000000","0","6344","0.000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","1.000000000000","0.000000000000","","","","",""
+"YI_RADIXOR","YI","LOWERCASE_GROUPS_ONLY","ALL_CANDIDATES","802","3578","0","3489","89","3","3676","802","6344","389","0","6392520","389","6392909","0.006085","0","6344","0.000000","0.942224862617","1.000000000000","0.999939151332","0.999939211655","0.999969575666","0.953239572064","0.970253116158","0.987885016662","0.942224862617","0.970682678643","0.970653145819","0.000060788345","","","","",""

docs/benchmarks/data/stemming-quality.sha256

@@ -0,0 +1 @@
+5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28  stemming-quality.csv

docs/benchmarks/index.md

@@ -6,6 +6,8 @@ two layers:
 - **benchmark reference pages**, which explain methodology, corpora, environment, candidate
   selection, and the English dictionary coverage experiment;
 - **language result pages**, which contain the actual same-language accuracy and throughput tables.
+- **pairwise quality pages and generated sections**, which publish over-stemming, under-stemming,
+  candidate-policy, classification, and partition measurements from one checked result snapshot.
 
 This structure keeps methodology separate from per-language result pages, while preserving all
 measured data and the command-class analysis for each Radixor language resource.
@@ -26,6 +28,9 @@ the preferred result measured by the accuracy pass.
 | Page | Purpose |
 | --- | --- |
 | [Methodology](reference/methodology.md) | Workload design, normalization, speed metrics, quality metrics, and interpretation rules. |
+| [Linguistic quality methodology](reference/linguistic-quality.md) | Gold-standard groups, output policies, pairwise formulas, ranking rules, aggregation, and limitations. |
+| [Tested stemmers](reference/tested-stemmers.md) | Versions, upstream attribution, evaluated coverage, adapters, preprocessing, and output capability. |
+| [Reproducibility and raw data](reference/reproducibility.md) | Commands, versioned CSV snapshot, checksum, generated artifacts, and unavailable provenance. |
 | [Corpora](reference/corpora.md) | Dictionary row counts, complete quality tokens, already-root tokens, changed speed tokens, and timing token counts. |
 | [Environment and reports](reference/environment.md) | Hardware, JVM, JMH settings, report files, and badge/report policy. |
 | [English dictionary coverage](reference/english-coverage.md) | Quality/speed operating curve for contracted Radixor tries built from 100% down to 10% of English dictionary rows. |
@@ -47,9 +52,276 @@ Open [Language Benchmark Pages](languages/index.md) for the complete language li
 
 The English dictionary coverage benchmark shows the current contracted-trie operating curve. With
 the full English dictionary, Radixor reaches `97.478%` all-token exactness and `97.197%`
-changed-token exactness at `109.8 ns/token`. Even with a deterministic 10% dictionary slice, it
+changed-token exactness at `135.8 ns/token`. Even with a deterministic 10% dictionary slice, it
-keeps `92.868%` all-token exactness and `76.516%` changed-token exactness at `90.9 ns/token`.
+keeps `92.868%` all-token exactness and `76.516%` changed-token exactness at `86.0 ns/token`.
 
 Those figures should not be reduced to a single speed badge. The professional interpretation is a
 quality/speed envelope: the amount and quality of dictionary knowledge affect stemming precision,
 while contracted tries reduce lookup cost in uniform regions of the compiled graph.
+
+## Quality versus performance
+
+Each language page keeps exact-root accuracy, JMH latency, and pairwise linguistic-quality results in separate tables. No undocumented scalar combines them. The current repository checkout does not contain the dated machine-readable JMH CSV files named by the performance provenance page, so this revision preserves the existing performance tables but does not regenerate a cross-language Pareto frontier from rounded Markdown values. A defensible Pareto analysis requires the original unrounded JMH snapshot on the same hardware and JVM. Readers can still inspect the quality and speed dimensions side by side on every language page.
+
+<!-- STEMMING-QUALITY-OVERVIEW:START -->
+
+## Pairwise Quality Findings
+
+The validated snapshot is a broad multilingual comparison covering the complete 20-language Radixor dictionary universe; 19 languages have existing benchmark pages. The direct ranking below uses only deterministic `PRIMARY_OUTPUT` rows over identical per-language inputs. Candidate-aware rows are intentionally excluded from this claim.
+
+!!! success "Evidence-based primary-output result"
+    Radixor achieved the highest balanced accuracy among the evaluated deterministic stemmers for every documented language in both `ALL_WORDS` and `LOWERCASE_GROUPS_ONLY`: **38 wins in 38 language-mode comparisons, with no exact first-place ties**. This statement is limited to the evaluated implementations, versions, dictionaries, adapters, and balanced-accuracy metric; it is not a universal claim about every stemming use case.
+
+### Per-language winner matrix
+
+| Language | Dictionary mode | Winner | Balanced accuracy | Runner-up | Difference | Exact tie | Deterministic stemmers |
+|---|---|---|---:|---|---:|---|---:|
+|Czech (`CS_CZ`)|ALL_WORDS|Radixor|0.996565|HUNSPELL CZECH LUCENE FILTER|0.142812638|no|3|
+|Czech (`CS_CZ`)|LOWERCASE_GROUPS_ONLY|Radixor|0.997139|HUNSPELL CZECH LUCENE FILTER|0.144369049|no|3|
+|Danish (`DA_DK`)|ALL_WORDS|Radixor|0.996066|SNOWBALL DANISH LUCENE FILTER|0.058096771|no|3|
+|Danish (`DA_DK`)|LOWERCASE_GROUPS_ONLY|Radixor|0.996305|SNOWBALL DANISH DIRECT|0.058230346|no|3|
+|Dutch (`NL_NL`)|ALL_WORDS|Radixor|0.988661|SNOWBALL DUTCH DIRECT|0.261574077|no|4|
+|Dutch (`NL_NL`)|LOWERCASE_GROUPS_ONLY|Radixor|0.989040|SNOWBALL DUTCH DIRECT|0.258544404|no|4|
+|English (`US_UK`)|ALL_WORDS|Radixor|0.965159|ENGLISH LUCENE PORTER COPIED|0.010532535|no|11|
+|English (`US_UK`)|LOWERCASE_GROUPS_ONLY|Radixor|0.965820|ENGLISH LUCENE PORTER COPIED|0.010920064|no|11|
+|Finnish (`FI_FI`)|ALL_WORDS|Radixor|0.984594|SNOWBALL FINNISH LUCENE FILTER|0.244241861|no|4|
+|Finnish (`FI_FI`)|LOWERCASE_GROUPS_ONLY|Radixor|0.988068|SNOWBALL FINNISH DIRECT|0.249668284|no|4|
+|French (`FR_FR`)|ALL_WORDS|Radixor|0.956992|SNOWBALL FRENCH DIRECT|0.111730673|no|6|
+|French (`FR_FR`)|LOWERCASE_GROUPS_ONLY|Radixor|0.957224|SNOWBALL FRENCH DIRECT|0.111809799|no|6|
+|German (`DE_DE`)|ALL_WORDS|Radixor|0.907901|GERMAN CISTEM|0.027131083|no|8|
+|German (`DE_DE`)|LOWERCASE_GROUPS_ONLY|Radixor|0.966157|GERMAN CISTEM|0.050868631|no|8|
+|Hungarian (`HU_HU`)|ALL_WORDS|Radixor|0.995491|SNOWBALL HUNGARIAN LUCENE FILTER|0.172884951|no|4|
+|Hungarian (`HU_HU`)|LOWERCASE_GROUPS_ONLY|Radixor|0.996163|SNOWBALL HUNGARIAN DIRECT|0.174455479|no|4|
+|Italian (`IT_IT`)|ALL_WORDS|Radixor|0.996507|SNOWBALL ITALIAN DIRECT|0.130318040|no|4|
+|Italian (`IT_IT`)|LOWERCASE_GROUPS_ONLY|Radixor|0.996512|SNOWBALL ITALIAN DIRECT|0.130307087|no|4|
+|Norwegian Bokmal (`NB_NO`)|ALL_WORDS|Radixor|0.974783|SNOWBALL NORWEGIAN BOKMAL DIRECT|0.099819340|no|5|
+|Norwegian Bokmal (`NB_NO`)|LOWERCASE_GROUPS_ONLY|Radixor|0.975000|SNOWBALL NORWEGIAN BOKMAL DIRECT|0.100008544|no|5|
+|Norwegian Nynorsk (`NN_NO`)|ALL_WORDS|Radixor|0.935777|SNOWBALL NORWEGIAN NYNORSK DIRECT|0.076868986|no|3|
+|Norwegian Nynorsk (`NN_NO`)|LOWERCASE_GROUPS_ONLY|Radixor|0.935853|SNOWBALL NORWEGIAN NYNORSK DIRECT|0.076816096|no|3|
+|Persian (`FA_IR`)|ALL_WORDS|Radixor|0.974922|PERSIAN LUCENE PERSIAN STEM FILTER|0.472751327|no|2|
+|Persian (`FA_IR`)|LOWERCASE_GROUPS_ONLY|Radixor|0.974922|PERSIAN LUCENE PERSIAN STEM FILTER|0.472751327|no|2|
+|Polish (`PL_PL`)|ALL_WORDS|Radixor|0.990388|POLISH LUCENE MORFOLOGIK FILTER|0.042233990|no|5|
+|Polish (`PL_PL`)|LOWERCASE_GROUPS_ONLY|Radixor|0.990579|POLISH LUCENE MORFOLOGIK FILTER|0.042401633|no|5|
+|Portuguese (`PT_PT`)|ALL_WORDS|Radixor|0.998502|SNOWBALL PORTUGUESE DIRECT|0.059701750|no|6|
+|Portuguese (`PT_PT`)|LOWERCASE_GROUPS_ONLY|Radixor|0.998502|SNOWBALL PORTUGUESE DIRECT|0.059701750|no|6|
+|Russian (`RU_RU`)|ALL_WORDS|Radixor|0.989827|SNOWBALL RUSSIAN LUCENE FILTER|0.154951419|no|4|
+|Russian (`RU_RU`)|LOWERCASE_GROUPS_ONLY|Radixor|0.989852|SNOWBALL RUSSIAN DIRECT|0.154997931|no|4|
+|Spanish (`ES_ES`)|ALL_WORDS|Radixor|0.989295|SNOWBALL SPANISH LUCENE FILTER|0.336680479|no|7|
+|Spanish (`ES_ES`)|LOWERCASE_GROUPS_ONLY|Radixor|0.989429|SNOWBALL SPANISH DIRECT|0.336708826|no|7|
+|Swedish (`SV_SE`)|ALL_WORDS|Radixor|0.974636|SNOWBALL SWEDISH DIRECT|0.167101450|no|5|
+|Swedish (`SV_SE`)|LOWERCASE_GROUPS_ONLY|Radixor|0.974584|SNOWBALL SWEDISH DIRECT|0.166984893|no|5|
+|Ukrainian (`UK_UA`)|ALL_WORDS|Radixor|0.995343|UKRAINIAN LUCENE MORFOLOGIK FILTER|0.066574583|no|4|
+|Ukrainian (`UK_UA`)|LOWERCASE_GROUPS_ONLY|Radixor|0.995342|UKRAINIAN LUCENE MORFOLOGIK FILTER|0.066590889|no|4|
+|Yiddish (`YI`)|ALL_WORDS|Radixor|0.988241|SNOWBALL YIDDISH DIRECT|0.097253207|no|3|
+|Yiddish (`YI`)|LOWERCASE_GROUPS_ONLY|Radixor|0.988241|SNOWBALL YIDDISH DIRECT|0.097253207|no|3|
+
+### Secondary-metric trade-offs
+
+Balanced-accuracy leadership does not imply leadership on every error trade-off. The table below lists all **15** deterministic primary-output language-mode-metric cases where a non-Radixor adapter has the best displayed value. Equal values are resolved by the authoritative row ordering and should be read as ties when the unrounded values are equal. Throughput leadership remains in the separate performance tables.
+
+<details class="quality-details" markdown="1"><summary>Non-Radixor secondary-metric leaders</summary>
+
+| Language | Dictionary mode | Metric | Leader | Value |
+|---|---|---|---|---:|
+|English|ALL_WORDS|Over-stemming percentage|ENGLISH LUCENE POSSESSIVE FILTER|0.000604|
+|English|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|ENGLISH LUCENE POSSESSIVE FILTER|0.000653|
+|French|ALL_WORDS|Over-stemming percentage|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|0.000177|
+|French|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|0.000166|
+|German|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|0.000188|
+|Italian|ALL_WORDS|Over-stemming percentage|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|0.000020|
+|Italian|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|0.000020|
+|Persian|ALL_WORDS|Over-stemming percentage|PERSIAN LUCENE PERSIAN STEM FILTER|0.002652|
+|Persian|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|PERSIAN LUCENE PERSIAN STEM FILTER|0.002652|
+|Portuguese|ALL_WORDS|Over-stemming percentage|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|0.000003|
+|Portuguese|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|0.000003|
+|Spanish|ALL_WORDS|Over-stemming percentage|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|0.000013|
+|Spanish|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|0.000012|
+|Ukrainian|ALL_WORDS|Over-stemming percentage|HUNSPELL UKRAINIAN LUCENE FILTER|0.000783|
+|Ukrainian|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|HUNSPELL UKRAINIAN LUCENE FILTER|0.000784|
+
+</details>
+
+### Win, tie, and placement summary
+
+Counts use `PRIMARY_OUTPUT` only and retain each adapter configuration as a separate stemmer except that language-specific Radixor identifiers are combined as Radixor. Coverage is displayed explicitly; unsupported languages are absent, not losses.
+
+<details class="quality-details" markdown="1"><summary>ALL_WORDS placements</summary>
+
+| Stemmer | Evaluated languages | Wins | Exact first-place ties | Top-three placements | Average rank | Median rank |
+|---|---:|---:|---:|---:|---:|---:|
+|Radixor|19|19|0|19|1.000|1.000|
+|CZECH LUCENE CZECH STEM FILTER|1|0|0|1|3.000|3.000|
+|ENGLISH LUCENE KSTEM FILTER|1|0|0|0|8.000|8.000|
+|ENGLISH LUCENE MINIMAL FILTER|1|0|0|0|9.000|9.000|
+|ENGLISH LUCENE PORTER COPIED|1|0|0|1|2.000|2.000|
+|ENGLISH LUCENE PORTER FILTER|1|0|0|1|3.000|3.000|
+|ENGLISH LUCENE POSSESSIVE FILTER|1|0|0|0|11.000|11.000|
+|ENGLISH OPENNLP PORTER|1|0|0|0|4.000|4.000|
+|ENGLISH PAICE HUSK LANCASTER|1|0|0|0|7.000|7.000|
+|ENGLISH SNOWBALL ORIGINAL PORTER|1|0|0|0|6.000|6.000|
+|ENGLISH SNOWBALL PORTER2|1|0|0|0|5.000|5.000|
+|FINNISH LUCENE FINNISH LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
+|FRENCH LUCENE FRENCH LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
+|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|1|0|0|0|6.000|6.000|
+|GERMAN CISTEM|1|0|0|1|2.000|2.000|
+|GERMAN LUCENE GERMAN LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
+|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|1|0|0|0|8.000|8.000|
+|GERMAN LUCENE GERMAN STEM FILTER|1|0|0|0|6.000|6.000|
+|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
+|HUNSPELL CZECH LUCENE FILTER|1|0|0|1|2.000|2.000|
+|HUNSPELL DUTCH LUCENE FILTER|1|0|0|1|3.000|3.000|
+|HUNSPELL ENGLISH LUCENE FILTER|1|0|0|0|10.000|10.000|
+|HUNSPELL FRENCH LUCENE FILTER|1|0|0|0|4.000|4.000|
+|HUNSPELL GERMAN LUCENE FILTER|1|0|0|0|7.000|7.000|
+|HUNSPELL POLISH LUCENE FILTER|1|0|0|1|3.000|3.000|
+|HUNSPELL SPANISH LUCENE FILTER|1|0|0|0|4.000|4.000|
+|HUNSPELL UKRAINIAN LUCENE FILTER|1|0|0|0|4.000|4.000|
+|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
+|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
+|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|1|0|0|0|5.000|5.000|
+|PERSIAN LUCENE PERSIAN STEM FILTER|1|0|0|1|2.000|2.000|
+|POLISH LUCENE MORFOLOGIK FILTER|1|0|0|1|2.000|2.000|
+|POLISH LUCENE STEMPEL DIRECT|1|0|0|0|4.000|4.000|
+|POLISH LUCENE STEMPEL FILTER|1|0|0|0|5.000|5.000|
+|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
+|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|1|0|0|0|6.000|6.000|
+|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|1|0|0|0|4.000|4.000|
+|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
+|SNOWBALL DANISH DIRECT|1|0|0|1|3.000|3.000|
+|SNOWBALL DANISH LUCENE FILTER|1|0|0|1|2.000|2.000|
+|SNOWBALL DUTCH DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL DUTCH LUCENE FILTER|1|0|0|0|4.000|4.000|
+|SNOWBALL FINNISH DIRECT|1|0|0|1|3.000|3.000|
+|SNOWBALL FINNISH LUCENE FILTER|1|0|0|1|2.000|2.000|
+|SNOWBALL FRENCH DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL FRENCH LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL GERMAN DIRECT|1|0|0|1|3.000|3.000|
+|SNOWBALL GERMAN LUCENE FILTER|1|0|0|0|4.000|4.000|
+|SNOWBALL HUNGARIAN DIRECT|1|0|0|1|3.000|3.000|
+|SNOWBALL HUNGARIAN LUCENE FILTER|1|0|0|1|2.000|2.000|
+|SNOWBALL ITALIAN DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL ITALIAN LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL NORWEGIAN BOKMAL DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL NORWEGIAN NYNORSK DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL PORTUGUESE DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL PORTUGUESE LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL RUSSIAN DIRECT|1|0|0|1|3.000|3.000|
+|SNOWBALL RUSSIAN LUCENE FILTER|1|0|0|1|2.000|2.000|
+|SNOWBALL SPANISH DIRECT|1|0|0|1|3.000|3.000|
+|SNOWBALL SPANISH LUCENE FILTER|1|0|0|1|2.000|2.000|
+|SNOWBALL SWEDISH DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL SWEDISH LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL YIDDISH DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL YIDDISH LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SPANISH LUCENE SPANISH LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
+|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|1|0|0|0|7.000|7.000|
+|SPANISH LUCENE SPANISH PLURAL STEM FILTER|1|0|0|0|6.000|6.000|
+|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
+|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|1|0|0|0|4.000|4.000|
+|UKRAINIAN LUCENE MORFOLOGIK FILTER|1|0|0|1|2.000|2.000|
+|UKRAINIAN MORFOLOGIK DIRECT|1|0|0|1|3.000|3.000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>LOWERCASE_GROUPS_ONLY placements</summary>
+
+| Stemmer | Evaluated languages | Wins | Exact first-place ties | Top-three placements | Average rank | Median rank |
+|---|---:|---:|---:|---:|---:|---:|
+|Radixor|19|19|0|19|1.000|1.000|
+|CZECH LUCENE CZECH STEM FILTER|1|0|0|1|3.000|3.000|
+|ENGLISH LUCENE KSTEM FILTER|1|0|0|0|8.000|8.000|
+|ENGLISH LUCENE MINIMAL FILTER|1|0|0|0|9.000|9.000|
+|ENGLISH LUCENE PORTER COPIED|1|0|0|1|2.000|2.000|
+|ENGLISH LUCENE PORTER FILTER|1|0|0|1|3.000|3.000|
+|ENGLISH LUCENE POSSESSIVE FILTER|1|0|0|0|11.000|11.000|
+|ENGLISH OPENNLP PORTER|1|0|0|0|4.000|4.000|
+|ENGLISH PAICE HUSK LANCASTER|1|0|0|0|7.000|7.000|
+|ENGLISH SNOWBALL ORIGINAL PORTER|1|0|0|0|6.000|6.000|
+|ENGLISH SNOWBALL PORTER2|1|0|0|0|5.000|5.000|
+|FINNISH LUCENE FINNISH LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
+|FRENCH LUCENE FRENCH LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
+|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|1|0|0|0|6.000|6.000|
+|GERMAN CISTEM|1|0|0|1|2.000|2.000|
+|GERMAN LUCENE GERMAN LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
+|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|1|0|0|0|8.000|8.000|
+|GERMAN LUCENE GERMAN STEM FILTER|1|0|0|0|6.000|6.000|
+|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
+|HUNSPELL CZECH LUCENE FILTER|1|0|0|1|2.000|2.000|
+|HUNSPELL DUTCH LUCENE FILTER|1|0|0|1|3.000|3.000|
+|HUNSPELL ENGLISH LUCENE FILTER|1|0|0|0|10.000|10.000|
+|HUNSPELL FRENCH LUCENE FILTER|1|0|0|0|4.000|4.000|
+|HUNSPELL GERMAN LUCENE FILTER|1|0|0|0|7.000|7.000|
+|HUNSPELL POLISH LUCENE FILTER|1|0|0|1|3.000|3.000|
+|HUNSPELL SPANISH LUCENE FILTER|1|0|0|0|4.000|4.000|
+|HUNSPELL UKRAINIAN LUCENE FILTER|1|0|0|0|4.000|4.000|
+|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
+|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
+|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|1|0|0|0|5.000|5.000|
+|PERSIAN LUCENE PERSIAN STEM FILTER|1|0|0|1|2.000|2.000|
+|POLISH LUCENE MORFOLOGIK FILTER|1|0|0|1|2.000|2.000|
+|POLISH LUCENE STEMPEL DIRECT|1|0|0|0|4.000|4.000|
+|POLISH LUCENE STEMPEL FILTER|1|0|0|0|5.000|5.000|
+|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
+|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|1|0|0|0|6.000|6.000|
+|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|1|0|0|0|4.000|4.000|
+|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
+|SNOWBALL DANISH DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL DANISH LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL DUTCH DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL DUTCH LUCENE FILTER|1|0|0|0|4.000|4.000|
+|SNOWBALL FINNISH DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL FINNISH LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL FRENCH DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL FRENCH LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL GERMAN DIRECT|1|0|0|1|3.000|3.000|
+|SNOWBALL GERMAN LUCENE FILTER|1|0|0|0|4.000|4.000|
+|SNOWBALL HUNGARIAN DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL HUNGARIAN LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL ITALIAN DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL ITALIAN LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL NORWEGIAN BOKMAL DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL NORWEGIAN NYNORSK DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL PORTUGUESE DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL PORTUGUESE LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL RUSSIAN DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL RUSSIAN LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL SPANISH DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL SPANISH LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL SWEDISH DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL SWEDISH LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SNOWBALL YIDDISH DIRECT|1|0|0|1|2.000|2.000|
+|SNOWBALL YIDDISH LUCENE FILTER|1|0|0|1|3.000|3.000|
+|SPANISH LUCENE SPANISH LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
+|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|1|0|0|0|7.000|7.000|
+|SPANISH LUCENE SPANISH PLURAL STEM FILTER|1|0|0|0|6.000|6.000|
+|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
+|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|1|0|0|0|4.000|4.000|
+|UKRAINIAN LUCENE MORFOLOGIK FILTER|1|0|0|1|2.000|2.000|
+|UKRAINIAN MORFOLOGIK DIRECT|1|0|0|1|3.000|3.000|
+
+</details>
+
+
+### Radixor full-coverage aggregates
+
+These aggregates cover all 19 documented languages. Macro balanced accuracy gives each language equal weight. Micro metrics first sum raw pair counts across languages. Unsupported third-party languages are never inserted as zero results, so this full-coverage table is not presented as a cross-stemmer common-language ranking.
+
+| Dictionary mode | Languages | Macro balanced accuracy | Micro balanced accuracy | Micro precision | Micro recall | Micro F1 |
+|---|---:|---:|---:|---:|---:|---:|
+|ALL_WORDS|19|0.978929|0.987664|0.975113|0.975328|0.975221|
+|LOWERCASE_GROUPS_ONLY|19|0.982354|0.989366|0.975322|0.978734|0.977025|
+
+### Reproducible data
+
+- [Machine-readable quality snapshot](data/stemming-quality.csv)
+- SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- [Linguistic quality methodology](reference/linguistic-quality.md)
+- [Tested stemmer inventory](reference/tested-stemmers.md)
+- [Reproducibility and raw data](reference/reproducibility.md)
+- Pearson and Spearman correlation files are generated under `build/reports/stemming-quality/`; they are separated by dictionary mode and output policy. Correlation does not establish metric equivalence.
+
+<!-- STEMMING-QUALITY-OVERVIEW:END -->

docs/benchmarks/languages/czech.md

@@ -26,11 +26,12 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
 | Radixor | 99.465% | 99.439% | 99.582% | Full Radixor dictionary patch-command stemmer. |
+| Lucene HunspellStemFilter | 84.850% | 82.269% | 96.806% | Benchmark-only Czech Hunspell dictionary compared via Lucene HunspellStemFilter. |
 | Lucene CzechStemFilter | 16.784% | 15.538% | 22.559% | Lucene Czech suffix stemmer implemented as a TokenFilter. |
 
 ## Speed
@@ -39,8 +40,9 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `czechRadixor` | 3.117 | 0.454 | 66.9 | 1.000 | Full Radixor dictionary patch-command stemmer. |
+| Radixor | `czechRadixor` | 3.332 | 0.240 | 71.6 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Lucene CzechStemFilter | `czechLuceneCzechStemFilter` | 2.921 | 0.202 | 62.7 | 0.937 | Czech suffix stemmer implemented as a Lucene TokenFilter. |
+| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 346.819 | 3.622 | 7448.4 | 104.091 | Benchmark-only Czech Hunspell dictionary compared via Lucene HunspellStemFilter. |
+| Lucene CzechStemFilter | `czechLuceneCzechStemFilter` | 3.163 | 0.253 | 67.9 | 0.949 | Czech suffix stemmer implemented as a Lucene TokenFilter. |
 
 ## Interpretation Notes
 
@@ -49,3 +51,368 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `CS_CZ` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/cs_cz/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.996565** among 3 deterministic stemmers. The runner-up is `HUNSPELL CZECH LUCENE FILTER` at 0.853752, a difference of 0.142813. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.997139** among 3 deterministic stemmers. The runner-up is `HUNSPELL CZECH LUCENE FILTER` at 0.852770, a difference of 0.144369. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **7 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.996565|3867 / 1334876815 (0.000290%)|2073 / 301835 (0.686799%)|0.988432|0.990189|0.990191|
+|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.853752|11408 / 1334876815 (0.000855%)|88283 / 301835 (29.248762%)|0.888560|0.810759|0.819499|
+|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.793614|14480 / 1334876815 (0.001085%)|124586 / 301835 (41.276194%)|0.829234|0.718241|0.736765|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.987264|0.993132|0.999997|0.996565|0.999996|0.000004|
+|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.949289|0.707512|0.999991|0.853752|0.999925|0.000075|
+|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.924477|0.587238|0.999989|0.793614|0.999896|0.000104|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.988432|0.990189|0.991953|0.980569|0.990194|0.990191|
+|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.888560|0.810759|0.745486|0.681745|0.819533|0.819499|
+|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.829234|0.718241|0.633453|0.560356|0.736809|0.736765|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.990187|0.998733|0.998686|0.998709|0.998709|
+|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.810723|0.995777|0.952852|0.973842|0.973842|
+|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.718192|0.993801|0.944977|0.968774|0.968774|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|299762|3867|2073|1334872948|3867 / 1334876815|2073 / 301835|
+|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|213552|11408|88283|1334865407|11408 / 1334876815|88283 / 301835|
+|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|177249|14480|124586|1334862335|14480 / 1334876815|124586 / 301835|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 1334876815 (0.000000%)|0 / 301835 (0.000000%)|1.000000|1.000000|1.000000|
+|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|0.871577|10102 / 1334876815 (0.000757%)|77523 / 301835 (25.683900%)|0.904855|0.836596|0.843258|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|0.956905|0.743161|0.999992|0.871577|0.999934|0.000066|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|0.904855|0.836596|0.777914|0.719094|0.843288|0.843258|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|301835|0|0|1334876815|0 / 1334876815|0 / 301835|
+|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|224312|10102|77523|1334866713|10102 / 1334876815|77523 / 301835|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999998|5850 / 1334876815 (0.000438%)|0 / 301835 (0.000000%)|0.984732|0.990402|0.990446|
+|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|0.871575|13917 / 1334876815 (0.001043%)|77523 / 301835 (25.683900%)|0.893851|0.830687|0.836477|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.980987|1.000000|0.999996|0.999998|0.999996|0.000004|
+|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|0.941581|0.743161|0.999990|0.871575|0.999932|0.000068|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.984732|0.990402|0.996139|0.980987|0.990448|0.990446|
+|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|0.893851|0.830687|0.775861|0.710406|0.836509|0.836477|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|301835|5850|0|1334870965|5850 / 1334876815|0 / 301835|
+|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|224312|13917|77523|1334862898|13917 / 1334876815|77523 / 301835|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|2073|3867|1983|596|1.153340%|4|52319|
+|HUNSPELL CZECH LUCENE FILTER|10760|1306|2509|3317|6.418840%|5|55596|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **7 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.997139|3863 / 1298544215 (0.000297%)|1709 / 298813 (0.571930%)|0.988580|0.990710|0.990714|
+|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.852770|11239 / 1298544215 (0.000866%)|87986 / 298813 (29.445171%)|0.888009|0.809505|0.818403|
+|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.791794|13950 / 1298544215 (0.001074%)|124426 / 298813 (41.640089%)|0.828709|0.715948|0.735055|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.987165|0.994281|0.999997|0.997139|0.999996|0.000004|
+|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.949389|0.705548|0.999991|0.852770|0.999924|0.000076|
+|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.925931|0.583599|0.999989|0.791794|0.999893|0.000107|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.988580|0.990710|0.992849|0.981591|0.990716|0.990714|
+|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.888009|0.809505|0.743753|0.679973|0.818437|0.818403|
+|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.828709|0.715948|0.630198|0.557569|0.735100|0.735055|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.990708|0.998726|0.999030|0.998878|0.998878|
+|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.809467|0.995812|0.952394|0.973619|0.973619|
+|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.715897|0.993897|0.944297|0.968463|0.968463|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|297104|3863|1709|1298540352|3863 / 1298544215|1709 / 298813|
+|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|210827|11239|87986|1298532976|11239 / 1298544215|87986 / 298813|
+|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|174387|13950|124426|1298530265|13950 / 1298544215|124426 / 298813|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 1298544215 (0.000000%)|0 / 298813 (0.000000%)|1.000000|1.000000|1.000000|
+|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|0.870432|10028 / 1298544215 (0.000772%)|77431 / 298813 (25.912862%)|0.904004|0.835052|0.841852|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|0.956666|0.740871|0.999992|0.870432|0.999933|0.000067|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|0.904004|0.835052|0.775874|0.716815|0.841883|0.841852|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|298813|0|0|1298544215|0 / 1298544215|0 / 298813|
+|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|221382|10028|77431|1298534187|10028 / 1298544215|77431 / 298813|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999998|5782 / 1298544215 (0.000445%)|0 / 298813 (0.000000%)|0.984756|0.990418|0.990461|
+|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|0.870430|13601 / 1298544215 (0.001047%)|77431 / 298813 (25.912862%)|0.893574|0.829463|0.835425|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.981017|1.000000|0.999996|0.999998|0.999996|0.000004|
+|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|0.942119|0.740871|0.999990|0.870430|0.999930|0.000070|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.984756|0.990418|0.996145|0.981017|0.990463|0.990461|
+|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|0.893574|0.829463|0.773936|0.708617|0.835457|0.835425|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|298813|5782|0|1298538433|5782 / 1298544215|0 / 298813|
+|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|221382|13601|77431|1298530614|13601 / 1298544215|77431 / 298813|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|1709|3863|1919|540|1.059488%|4|51543|
+|HUNSPELL CZECH LUCENE FILTER|10555|1211|2362|3237|6.351044%|5|54804|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `CS_CZ`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/danish.md

@@ -26,11 +26,11 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
-| Radixor | 99.371% | 99.527% | 98.923% | Radixor baseline in the Snowball-language comparison family. |
+| Radixor | 99.371% | 99.527% | 98.923% | Full Radixor dictionary patch-command stemmer. |
 | Lucene SnowballFilter | 55.509% | 54.159% | 59.371% | Lucene TokenFilter integration path around the Snowball algorithm. |
 | Official Snowball direct | 55.509% | 54.159% | 59.371% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
 
@@ -40,9 +40,9 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `radixor[DANISH]` | 1.065 | 0.019 | 44.6 | 1.000 | Radixor baseline for the Snowball-language comparison family. |
+| Radixor | `radixor[DANISH]` | 1.143 | 0.017 | 47.8 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Official Snowball direct | `snowballDirect[DANISH]` | 2.028 | 0.011 | 84.9 | 1.904 | Official Snowball generated Java stemmer; direct API. |
+| Official Snowball direct | `snowballDirect[DANISH]` | 2.168 | 0.058 | 90.7 | 1.896 | Official Snowball generated Java stemmer; direct API. |
-| Lucene SnowballFilter | `luceneSnowballFilter[DANISH]` | 2.692 | 0.028 | 112.6 | 2.527 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Lucene SnowballFilter | `luceneSnowballFilter[DANISH]` | 2.975 | 0.143 | 124.5 | 2.602 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
 
 ## Interpretation Notes
 
@@ -51,3 +51,346 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `DA_DK` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/da_dk/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.996066** among 3 deterministic stemmers. The runner-up is `SNOWBALL DANISH LUCENE FILTER` at 0.937969, a difference of 0.058097. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.996305** among 3 deterministic stemmers. The runner-up is `SNOWBALL DANISH DIRECT` at 0.938074, a difference of 0.058230. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **5 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.996066|1165 / 394111186 (0.000296%)|707 / 89895 (0.786473%)|0.988108|0.989614|0.989615|
+|2|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.937969|6507 / 394111186 (0.001651%)|11151 / 89895 (12.404472%)|0.913718|0.899181|0.899475|
+|3|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.937903|6341 / 394111186 (0.001609%)|11163 / 89895 (12.417821%)|0.915090|0.899959|0.900279|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.987106|0.992135|0.999997|0.996066|0.999995|0.000005|
+|2|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.923672|0.875955|0.999983|0.937969|0.999955|0.000045|
+|3|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.925464|0.875822|0.999984|0.937903|0.999956|0.000044|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.988108|0.989614|0.991125|0.979442|0.989618|0.989615|
+|2|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.913718|0.899181|0.885100|0.816830|0.899498|0.899475|
+|3|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.915090|0.899959|0.885320|0.818114|0.900301|0.900279|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.989612|0.998466|0.998719|0.998592|0.998592|
+|2|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.899159|0.994053|0.978603|0.986268|0.986268|
+|3|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.899937|0.994196|0.978579|0.986326|0.986326|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|89188|1165|707|394110021|1165 / 394111186|707 / 89895|
+|2|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|78744|6507|11151|394104679|6507 / 394111186|11151 / 89895|
+|3|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|78732|6341|11163|394104845|6341 / 394111186|11163 / 89895|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 394111186 (0.000000%)|0 / 89895 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|89895|0|0|394111186|0 / 394111186|0 / 89895|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999998|1849 / 394111186 (0.000469%)|0 / 89895 (0.000000%)|0.983812|0.989820|0.989869|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.979846|1.000000|0.999995|0.999998|0.999995|0.000005|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.983812|0.989820|0.995903|0.979846|0.989872|0.989869|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|89895|1849|0|394109337|1849 / 394111186|0 / 89895|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|707|1165|684|323|1.150326%|3|28405|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **5 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.996305|1165 / 392820788 (0.000297%)|663 / 89740 (0.738801%)|0.988190|0.989843|0.989845|
+|2|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.938074|6341 / 392820788 (0.001614%)|11113 / 89740 (12.383552%)|0.915093|0.900096|0.900410|
+|3|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.938074|6341 / 392820788 (0.001614%)|11113 / 89740 (12.383552%)|0.915093|0.900096|0.900410|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.987090|0.992612|0.999997|0.996305|0.999995|0.000005|
+|2|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.925372|0.876164|0.999984|0.938074|0.999956|0.000044|
+|3|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.925372|0.876164|0.999984|0.938074|0.999956|0.000044|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.988190|0.989843|0.991503|0.979891|0.989847|0.989845|
+|2|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.915093|0.900096|0.885583|0.818341|0.900432|0.900410|
+|3|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.915093|0.900096|0.885583|0.818341|0.900432|0.900410|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.989841|0.998463|0.998812|0.998637|0.998637|
+|2|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.900074|0.994185|0.978644|0.986354|0.986354|
+|3|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.900074|0.994185|0.978644|0.986354|0.986354|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|89077|1165|663|392819623|1165 / 392820788|663 / 89740|
+|2|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|78627|6341|11113|392814447|6341 / 392820788|11113 / 89740|
+|3|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|78627|6341|11113|392814447|6341 / 392820788|11113 / 89740|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 392820788 (0.000000%)|0 / 89740 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|89740|0|0|392820788|0 / 392820788|0 / 89740|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999998|1849 / 392820788 (0.000471%)|0 / 89740 (0.000000%)|0.983784|0.989803|0.989852|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.979812|1.000000|0.999995|0.999998|0.999995|0.000005|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.983784|0.989803|0.995896|0.979812|0.989855|0.989852|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|89740|1849|0|392818939|1849 / 392820788|0 / 89740|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|663|1165|684|315|1.123676%|3|28351|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `DA_DK`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/dutch.md

@@ -26,11 +26,12 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
-| Radixor | 99.120% | 98.711% | 100.000% | Radixor baseline in the Snowball-language comparison family. |
+| Radixor | 99.120% | 98.711% | 100.000% | Full Radixor dictionary patch-command stemmer. |
+| Lucene HunspellStemFilter | 46.590% | 22.718% | 97.976% | Benchmark-only Dutch Hunspell dictionary compared via Lucene HunspellStemFilter. |
 | Official Snowball direct | 15.954% | 8.992% | 30.939% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
 | Lucene SnowballFilter | 12.620% | 5.441% | 28.073% | Lucene TokenFilter integration path around the Snowball algorithm. |
 
@@ -40,9 +41,10 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `radixor[DUTCH]` | 1.262 | 0.039 | 58.7 | 1.000 | Radixor baseline for the Snowball-language comparison family. |
+| Radixor | `radixor[DUTCH]` | 1.331 | 0.114 | 61.9 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Official Snowball direct | `snowballDirect[DUTCH]` | 3.968 | 0.258 | 184.7 | 3.145 | Official Snowball generated Java stemmer; direct API. |
+| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 22.760 | 1.387 | 1059.3 | 17.105 | Benchmark-only Dutch Hunspell dictionary compared via Lucene HunspellStemFilter. |
-| Lucene SnowballFilter | `luceneSnowballFilter[DUTCH]` | 6.866 | 0.337 | 319.6 | 5.441 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Official Snowball direct | `snowballDirect[DUTCH]` | 4.146 | 0.291 | 193.0 | 3.116 | Official Snowball generated Java stemmer; direct API. |
+| Lucene SnowballFilter | `luceneSnowballFilter[DUTCH]` | 7.375 | 0.595 | 343.3 | 5.543 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
 
 ## Interpretation Notes
 
@@ -51,3 +53,378 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `NL_NL` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/nl_nl/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.988661** among 4 deterministic stemmers. The runner-up is `SNOWBALL DUTCH DIRECT` at 0.727087, a difference of 0.261574. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.989040** among 4 deterministic stemmers. The runner-up is `SNOWBALL DUTCH DIRECT` at 0.730495, a difference of 0.258544. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **8 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.988661|1214 / 350437960 (0.000346%)|1464 / 64566 (2.267447%)|0.980362|0.979221|0.979219|
+|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.727087|4382 / 350437960 (0.001250%)|35241 / 64566 (54.581359%)|0.735353|0.596807|0.628557|
+|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.643123|1333 / 350437960 (0.000380%)|46084 / 64566 (71.375027%)|0.642512|0.438061|0.516674|
+|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.618497|1588 / 350437960 (0.000453%)|49264 / 64566 (76.300220%)|0.579068|0.375712|0.463333|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.981124|0.977326|0.999997|0.988661|0.999992|0.000008|
+|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.869997|0.454186|0.999987|0.727087|0.999887|0.000113|
+|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.932728|0.286250|0.999996|0.643123|0.999865|0.000135|
+|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.905980|0.236998|0.999995|0.618497|0.999855|0.000145|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.980362|0.979221|0.978083|0.959289|0.979223|0.979219|
+|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.735353|0.596807|0.502190|0.425321|0.628602|0.628557|
+|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.642512|0.438061|0.332316|0.280459|0.516714|0.516674|
+|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.579068|0.375712|0.278062|0.231309|0.463374|0.463333|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.979217|0.997464|0.997003|0.997234|0.997234|
+|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.596756|0.992815|0.917346|0.953590|0.953590|
+|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.438012|0.996932|0.889026|0.939892|0.939892|
+|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.375664|0.995828|0.888410|0.939057|0.939057|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|63102|1214|1464|350436746|1214 / 350437960|1464 / 64566|
+|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|29325|4382|35241|350433578|4382 / 350437960|35241 / 64566|
+|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|18482|1333|46084|350436627|1333 / 350437960|46084 / 64566|
+|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|15302|1588|49264|350436372|1588 / 350437960|49264 / 64566|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 350437960 (0.000000%)|0 / 64566 (0.000000%)|1.000000|1.000000|1.000000|
+|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|0.665519|1164 / 350437960 (0.000332%)|43192 / 64566 (66.895889%)|0.690741|0.490770|0.560268|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|0.948354|0.331041|0.999997|0.665519|0.999873|0.000127|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|0.690741|0.490770|0.380588|0.325179|0.560307|0.560268|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|64566|0|0|350437960|0 / 350437960|0 / 64566|
+|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|21374|1164|43192|350436796|1164 / 350437960|43192 / 64566|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999996|2651 / 350437960 (0.000756%)|0 / 64566 (0.000000%)|0.968198|0.979884|0.980078|
+|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|0.665518|1738 / 350437960 (0.000496%)|43192 / 64566 (66.895889%)|0.680640|0.487557|0.553265|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.960561|1.000000|0.999992|0.999996|0.999992|0.000008|
+|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|0.924801|0.331041|0.999995|0.665518|0.999872|0.000128|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.968198|0.979884|0.991855|0.960561|0.980082|0.980078|
+|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|0.680640|0.487557|0.379812|0.322364|0.553306|0.553265|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|64566|2651|0|350435309|2651 / 350437960|0 / 64566|
+|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|21374|1738|43192|350436222|1738 / 350437960|43192 / 64566|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|HUNSPELL DUTCH LUCENE FILTER|2892|169|405|1254|4.736186%|3|27763|
+|Radixor|1464|1214|1437|572|2.160366%|3|27061|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **8 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.989040|1214 / 329603856 (0.000368%)|1384 / 63147 (2.191711%)|0.980194|0.979401|0.979398|
+|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.730495|4382 / 329603856 (0.001329%)|34036 / 63147 (53.899631%)|0.738412|0.602463|0.632953|
+|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.645159|1310 / 329603856 (0.000397%)|44814 / 63147 (70.967742%)|0.646808|0.442880|0.520498|
+|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.618546|1544 / 329603856 (0.000468%)|48175 / 63147 (76.290243%)|0.579362|0.375883|0.463566|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.980723|0.978083|0.999996|0.989040|0.999992|0.000008|
+|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.869167|0.461004|0.999987|0.730495|0.999883|0.000117|
+|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.933310|0.290323|0.999996|0.645159|0.999860|0.000140|
+|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.906515|0.237098|0.999995|0.618546|0.999849|0.000151|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.980194|0.979401|0.978610|0.959634|0.979402|0.979398|
+|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.738412|0.602463|0.508789|0.431089|0.633000|0.632953|
+|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.646808|0.442880|0.336718|0.284422|0.520539|0.520498|
+|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.579362|0.375883|0.278182|0.231439|0.463608|0.463566|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.979397|0.997373|0.997139|0.997256|0.997256|
+|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.602410|0.992557|0.918059|0.953856|0.953856|
+|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.442829|0.996884|0.889061|0.939890|0.939890|
+|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.375834|0.995817|0.887492|0.938539|0.938539|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|61763|1214|1384|329602642|1214 / 329603856|1384 / 63147|
+|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|29111|4382|34036|329599474|4382 / 329603856|34036 / 63147|
+|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|18333|1310|44814|329602546|1310 / 329603856|44814 / 63147|
+|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|14972|1544|48175|329602312|1544 / 329603856|48175 / 63147|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 329603856 (0.000000%)|0 / 63147 (0.000000%)|1.000000|1.000000|1.000000|
+|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|0.667956|1141 / 329603856 (0.000346%)|41935 / 63147 (66.408539%)|0.695206|0.496187|0.564555|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|0.948955|0.335915|0.999997|0.667956|0.999869|0.000131|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|0.695206|0.496187|0.385755|0.329953|0.564595|0.564555|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|63147|0|0|329603856|0 / 329603856|0 / 63147|
+|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|21212|1141|41935|329602715|1141 / 329603856|41935 / 63147|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999996|2651 / 329603856 (0.000804%)|0 / 63147 (0.000000%)|0.967506|0.979441|0.979644|
+|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|0.667955|1712 / 329603856 (0.000519%)|41935 / 63147 (66.408539%)|0.684952|0.492895|0.557477|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.959710|1.000000|0.999992|0.999996|0.999992|0.000008|
+|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|0.925318|0.335915|0.999995|0.667955|0.999868|0.000132|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.967506|0.979441|0.991674|0.959710|0.979648|0.979644|
+|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|0.684952|0.492895|0.384956|0.327048|0.557519|0.557477|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|63147|2651|0|329601205|2651 / 329603856|0 / 63147|
+|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|21212|1712|41935|329602144|1712 / 329603856|41935 / 63147|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|HUNSPELL DUTCH LUCENE FILTER|2879|169|402|1186|4.618740%|3|26896|
+|Radixor|1384|1214|1437|549|2.138017%|3|26239|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `NL_NL`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/english.md

@@ -26,13 +26,14 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
 | Radixor | 97.478% | 97.197% | 97.552% | Full Radixor dictionary patch-command stemmer. |
 | Lucene EnglishMinimalStemFilter | 90.981% | 65.189% | 97.820% | Minimal English plural reduction, not a full stemmer. |
 | Lucene KStemFilter | 80.076% | 76.608% | 80.996% | Krovetz-style English stemming TokenFilter; broader than minimal suffix reducers. |
+| Lucene HunspellStemFilter | 80.243% | 12.750% | 98.139% | Benchmark-only English Hunspell dictionary compared via Lucene HunspellStemFilter. |
 | Lucene EnglishPossessiveFilter | 79.032% | 0.003% | 99.987% | Possessive-ending remover only, not a full stemmer. |
 | Snowball English / Porter2 | 40.342% | 46.296% | 38.763% | Porter2 rule-based suffix stemmer, distinct from original Porter. |
 | Lucene PorterStemFilter | 39.538% | 46.201% | 37.772% | Lucene TokenFilter path for Porter suffix rules; not dictionary-root equivalent. |
@@ -47,16 +48,17 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `radixorUsUkProfiPreferredStem` | 16.621 | 8.532 | 79.0 | 1.000 | Full dictionary patch-command stemmer using compiled patch commands. |
+| Radixor | `radixorUsUkProfiPreferredStem` | 21.987 | 8.707 | 104.5 | 1.000 | Full dictionary patch-command stemmer using compiled patch commands. |
-| Lucene EnglishPossessiveFilter | `luceneEnglishPossessiveFilter` | 23.845 | 0.833 | 113.3 | 1.435 | Possessive-ending remover only; not a full stemmer. |
+| Lucene EnglishPossessiveFilter | `luceneEnglishPossessiveFilter` | 24.539 | 1.515 | 116.6 | 1.116 | Possessive-ending remover only; not a full stemmer. |
-| Lucene EnglishMinimalStemFilter | `luceneEnglishMinimalStemFilter` | 17.091 | 0.198 | 81.2 | 1.028 | Narrow plural reduction filter; not a full stemmer. |
+| Lucene EnglishMinimalStemFilter | `luceneEnglishMinimalStemFilter` | 22.702 | 1.195 | 107.8 | 1.032 | Narrow plural reduction filter; not a full stemmer. |
-| Lucene PorterStemmer direct copy | `lucenePorterStemmerCopied` | 18.598 | 10.954 | 88.4 | 1.119 | Benchmark-only generated copy of Lucene package-private Porter implementation. |
+| Lucene PorterStemmer direct copy | `lucenePorterStemmerCopied` | 24.696 | 13.235 | 117.3 | 1.123 | Benchmark-only generated copy of Lucene package-private Porter implementation. |
-| OpenNLP PorterStemmer | `opennlpPorterStemmer` | 18.213 | 10.674 | 86.5 | 1.096 | Apache OpenNLP Porter implementation. |
+| OpenNLP PorterStemmer | `opennlpPorterStemmer` | 23.121 | 12.528 | 109.8 | 1.052 | Apache OpenNLP Porter implementation. |
-| Snowball original Porter | `snowballOriginalPorter` | 32.921 | 11.520 | 156.4 | 1.981 | Classic Porter suffix-rule stemmer; historical English baseline, not a dictionary-equivalent stemmer. |
+| Snowball original Porter | `snowballOriginalPorter` | 38.904 | 10.353 | 184.8 | 1.769 | Classic Porter suffix-rule stemmer; historical English baseline, not a dictionary-equivalent stemmer. |
-| Lucene PorterStemFilter | `lucenePorterStemFilter` | 42.874 | 1.321 | 203.7 | 2.579 | Lucene TokenFilter integration path for Porter; includes TokenStream overhead. |
+| Lucene PorterStemFilter | `lucenePorterStemFilter` | 37.021 | 1.196 | 175.9 | 1.684 | Lucene TokenFilter integration path for Porter; includes TokenStream overhead. |
-| Lucene KStemFilter | `luceneKStemFilter` | 50.483 | 3.624 | 239.8 | 3.037 | Krovetz-style English TokenFilter; broader than minimal suffix filters. |
+| Lucene KStemFilter | `luceneKStemFilter` | 51.640 | 2.591 | 245.3 | 2.349 | Krovetz-style English TokenFilter; broader than minimal suffix filters. |
-| Snowball English / Porter2 | `snowballEnglishPorter2` | 47.844 | 1.887 | 227.3 | 2.878 | Porter2 suffix-rule stemmer, distinct from original Porter. |
+| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 79.785 | 1.347 | 379.0 | 3.629 | Benchmark-only English Hunspell comparison using the benchmark Hunspell corpus. |
-| Paice/Husk Lancaster | `paiceHuskLancaster` | 135.050 | 11.088 | 641.6 | 8.125 | Aggressive rule-based English stemmer. |
+| Snowball English / Porter2 | `snowballEnglishPorter2` | 52.437 | 0.773 | 249.1 | 2.385 | Porter2 suffix-rule stemmer, distinct from original Porter. |
+| Paice/Husk Lancaster | `paiceHuskLancaster` | 141.556 | 12.324 | 672.5 | 6.438 | Aggressive rule-based English stemmer. |
 
 ## Interpretation Notes
 
@@ -65,3 +67,448 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `US_UK` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/us_uk/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.965159** among 11 deterministic stemmers. The runner-up is `ENGLISH LUCENE PORTER COPIED` at 0.954627, a difference of 0.010533. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.965820** among 11 deterministic stemmers. The runner-up is `ENGLISH LUCENE PORTER COPIED` at 0.954900, a difference of 0.010920. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **15 result rows**, **11 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.965159|1149886 / 184490451771 (0.000623%)|21869 / 313870 (6.967534%)|0.240076|0.332621|0.434052|
+|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.954627|1557406 / 184490451771 (0.000844%)|28480 / 313870 (9.073820%)|0.185679|0.264659|0.375252|
+|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.954627|1557406 / 184490451771 (0.000844%)|28480 / 313870 (9.073820%)|0.185679|0.264659|0.375252|
+|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.954627|1557406 / 184490451771 (0.000844%)|28480 / 313870 (9.073820%)|0.185679|0.264659|0.375252|
+|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.954537|1566711 / 184490451771 (0.000849%)|28536 / 313870 (9.091662%)|0.184753|0.263477|0.374240|
+|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.954490|1555293 / 184490451771 (0.000843%)|28566 / 313870 (9.101220%)|0.185835|0.264849|0.375363|
+|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.952394|3062661 / 184490451771 (0.001660%)|29879 / 313870 (9.519546%)|0.103643|0.155164|0.277089|
+|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.878441|1368501 / 184490451771 (0.000742%)|76305 / 313870 (24.311020%)|0.176284|0.247472|0.334598|
+|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.718599|1122264 / 184490451771 (0.000608%)|176645 / 313870 (56.279670%)|0.128204|0.174436|0.218251|
+|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.573277|1981986 / 184490451771 (0.001074%)|267868 / 313870 (85.343614%)|0.027298|0.039287|0.057655|
+|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.500008|1115154 / 184490451771 (0.000604%)|313863 / 313870 (99.997770%)|0.000007|0.000010|0.000009|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.202513|0.930325|0.999994|0.965159|0.999994|0.000006|
+|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.154868|0.909262|0.999992|0.954627|0.999991|0.000009|
+|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.154868|0.909262|0.999992|0.954627|0.999991|0.000009|
+|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.154868|0.909262|0.999992|0.954627|0.999991|0.000009|
+|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.154064|0.909083|0.999992|0.954537|0.999991|0.000009|
+|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.155006|0.908988|0.999992|0.954490|0.999991|0.000009|
+|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.084858|0.904805|0.999983|0.952394|0.999983|0.000017|
+|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.147917|0.756890|0.999993|0.878441|0.999992|0.000008|
+|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.108953|0.437203|0.999994|0.718599|0.999993|0.000007|
+|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.022684|0.146564|0.999989|0.573277|0.999988|0.000012|
+|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.000006|0.000022|0.999994|0.500008|0.999992|0.000008|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.240076|0.332621|0.541270|0.199487|0.434054|0.434052|
+|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.185679|0.264659|0.460563|0.152511|0.375254|0.375252|
+|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.185679|0.264659|0.460563|0.152511|0.375254|0.375252|
+|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.185679|0.264659|0.460563|0.152511|0.375254|0.375252|
+|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.184753|0.263477|0.459102|0.151727|0.374242|0.374240|
+|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.185835|0.264849|0.460751|0.152637|0.375365|0.375363|
+|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.103643|0.155164|0.308543|0.084107|0.277092|0.277089|
+|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.176284|0.247472|0.415099|0.141208|0.334600|0.334598|
+|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.128204|0.174436|0.272816|0.095552|0.218253|0.218251|
+|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.027298|0.039287|0.070051|0.020037|0.057659|0.057655|
+|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.000007|0.000010|0.000015|0.000005|0.000012|0.000009|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.332619|0.994215|0.997770|0.995989|0.995989|
+|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.264656|0.969648|0.997199|0.983231|0.983231|
+|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.264656|0.969648|0.997199|0.983231|0.983231|
+|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.264656|0.969648|0.997199|0.983231|0.983231|
+|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.263474|0.969037|0.997182|0.982908|0.982908|
+|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.264847|0.969891|0.997193|0.983353|0.983353|
+|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.155162|0.937768|0.996600|0.966289|0.966289|
+|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.247470|0.980687|0.992108|0.986364|0.986364|
+|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.174433|0.995202|0.981174|0.988138|0.988138|
+|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.039284|0.993096|0.963677|0.978166|0.978166|
+|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.000007|0.995789|0.958019|0.976539|0.976539|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|292001|1149886|21869|184489301885|1149886 / 184490451771|21869 / 313870|
+|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|285390|1557406|28480|184488894365|1557406 / 184490451771|28480 / 313870|
+|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|285390|1557406|28480|184488894365|1557406 / 184490451771|28480 / 313870|
+|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|285390|1557406|28480|184488894365|1557406 / 184490451771|28480 / 313870|
+|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|285334|1566711|28536|184488885060|1566711 / 184490451771|28536 / 313870|
+|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|285304|1555293|28566|184488896478|1555293 / 184490451771|28566 / 313870|
+|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|283991|3062661|29879|184487389110|3062661 / 184490451771|29879 / 313870|
+|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|237565|1368501|76305|184489083270|1368501 / 184490451771|76305 / 313870|
+|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|137225|1122264|176645|184489329507|1122264 / 184490451771|176645 / 313870|
+|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|46002|1981986|267868|184488469785|1981986 / 184490451771|267868 / 313870|
+|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|7|1115154|313863|184489336617|1115154 / 184490451771|313863 / 313870|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|0.999976|12 / 184490451771 (0.000000%)|15 / 313870 (0.004779%)|0.999960|0.999957|0.999957|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|0.581603|1978852 / 184490451771 (0.001073%)|262641 / 313870 (83.678274%)|0.030370|0.043712|0.064174|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|0.999962|0.999952|1.000000|0.999976|1.000000|0.000000|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|0.025235|0.163217|0.999989|0.581603|0.999988|0.000012|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|0.999960|0.999957|0.999954|0.999914|0.999957|0.999957|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|0.030370|0.043712|0.077961|0.022344|0.064178|0.064174|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|313855|12|15|184490451759|12 / 184490451771|15 / 313870|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|51229|1978852|262641|184488472919|1978852 / 184490451771|262641 / 313870|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999945|11482166 / 184490451771 (0.006224%)|15 / 313870 (0.004779%)|0.033039|0.051834|0.163107|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|0.581603|2008917 / 184490451771 (0.001089%)|262641 / 313870 (83.678274%)|0.029943|0.043158|0.063704|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.026607|0.999952|0.999938|0.999945|0.999938|0.000062|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|0.024867|0.163217|0.999989|0.581603|0.999988|0.000012|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.033039|0.051834|0.120237|0.026607|0.163112|0.163107|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|0.029943|0.043158|0.077254|0.022055|0.063708|0.063704|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|313855|11482166|15|184478969605|11482166 / 184490451771|15 / 313870|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|51229|2008917|262641|184488442854|2008917 / 184490451771|262641 / 313870|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|21854|1149874|10332280|29208|4.808384%|1355|2838145|
+|HUNSPELL ENGLISH LUCENE FILTER|5227|3134|26931|6837|1.125545%|4|614296|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **15 result rows**, **11 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.965820|1148489 / 170474840204 (0.000674%)|21319 / 311891 (6.835401%)|0.239424|0.331902|0.433722|
+|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.954900|1552702 / 170474840204 (0.000911%)|28130 / 311891 (9.019177%)|0.185277|0.264166|0.374937|
+|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.954900|1552702 / 170474840204 (0.000911%)|28130 / 311891 (9.019177%)|0.185277|0.264166|0.374937|
+|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.954900|1552702 / 170474840204 (0.000911%)|28130 / 311891 (9.019177%)|0.185277|0.264166|0.374937|
+|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.954850|1561891 / 170474840204 (0.000916%)|28161 / 311891 (9.029116%)|0.184375|0.263016|0.373964|
+|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.954762|1550615 / 170474840204 (0.000910%)|28216 / 311891 (9.046750%)|0.185431|0.264353|0.375045|
+|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.952710|3045870 / 170474840204 (0.001787%)|29493 / 311891 (9.456188%)|0.103633|0.155157|0.277170|
+|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.880820|1367069 / 170474840204 (0.000802%)|74340 / 311891 (23.835250%)|0.176477|0.247899|0.335789|
+|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.719516|1120871 / 170474840204 (0.000657%)|174959 / 311891 (56.096200%)|0.128139|0.174470|0.218621|
+|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.573619|1978041 / 170474840204 (0.001160%)|265965 / 311891 (85.274984%)|0.027312|0.039323|0.057799|
+|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.500005|1113773 / 170474840204 (0.000653%)|311886 / 311891 (99.998397%)|0.000005|0.000007|0.000005|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.201918|0.931646|0.999993|0.965820|0.999993|0.000007|
+|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.154515|0.909808|0.999991|0.954900|0.999991|0.000009|
+|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.154515|0.909808|0.999991|0.954900|0.999991|0.000009|
+|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.154515|0.909808|0.999991|0.954900|0.999991|0.000009|
+|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.153731|0.909709|0.999991|0.954850|0.999991|0.000009|
+|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.154651|0.909532|0.999991|0.954762|0.999991|0.000009|
+|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.084848|0.905438|0.999982|0.952710|0.999982|0.000018|
+|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.148042|0.761647|0.999992|0.880820|0.999992|0.000008|
+|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.108866|0.439038|0.999993|0.719516|0.999992|0.000008|
+|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.022691|0.147250|0.999988|0.573619|0.999987|0.000013|
+|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.000004|0.000016|0.999993|0.500005|0.999992|0.000008|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.239424|0.331902|0.540775|0.198970|0.433723|0.433722|
+|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.185277|0.264166|0.460049|0.152184|0.374939|0.374937|
+|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.185277|0.264166|0.460049|0.152184|0.374939|0.374937|
+|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.185277|0.264166|0.460049|0.152184|0.374939|0.374937|
+|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.184375|0.263016|0.458637|0.151421|0.373966|0.373964|
+|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.185431|0.264353|0.460234|0.152308|0.375047|0.375045|
+|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.103633|0.155157|0.308576|0.084103|0.277173|0.277170|
+|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.176477|0.247899|0.416437|0.141487|0.335791|0.335789|
+|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.128139|0.174470|0.273277|0.095572|0.218624|0.218621|
+|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.027312|0.039323|0.070190|0.020056|0.057804|0.057799|
+|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.000005|0.000007|0.000011|0.000004|0.000008|0.000005|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.331900|0.993959|0.997731|0.995842|0.995842|
+|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.264164|0.968645|0.997109|0.982671|0.982671|
+|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.264164|0.968645|0.997109|0.982671|0.982671|
+|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.264164|0.968645|0.997109|0.982671|0.982671|
+|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.263014|0.968020|0.997096|0.982343|0.982343|
+|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.264351|0.968894|0.997102|0.982795|0.982795|
+|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.155154|0.936077|0.996487|0.965338|0.965338|
+|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.247897|0.979822|0.991991|0.985869|0.985869|
+|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.174467|0.994994|0.980520|0.987704|0.987704|
+|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.039320|0.993066|0.962317|0.977450|0.977450|
+|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.000004|0.995605|0.956423|0.975621|0.975621|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|290572|1148489|21319|170473691715|1148489 / 170474840204|21319 / 311891|
+|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|283761|1552702|28130|170473287502|1552702 / 170474840204|28130 / 311891|
+|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|283761|1552702|28130|170473287502|1552702 / 170474840204|28130 / 311891|
+|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|283761|1552702|28130|170473287502|1552702 / 170474840204|28130 / 311891|
+|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|283730|1561891|28161|170473278313|1561891 / 170474840204|28161 / 311891|
+|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|283675|1550615|28216|170473289589|1550615 / 170474840204|28216 / 311891|
+|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|282398|3045870|29493|170471794334|3045870 / 170474840204|29493 / 311891|
+|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|237551|1367069|74340|170473473135|1367069 / 170474840204|74340 / 311891|
+|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|136932|1120871|174959|170473719333|1120871 / 170474840204|174959 / 311891|
+|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|45926|1978041|265965|170472862163|1978041 / 170474840204|265965 / 311891|
+|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|5|1113773|311886|170473726431|1113773 / 170474840204|311886 / 311891|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 170474840204 (0.000000%)|0 / 311891 (0.000000%)|1.000000|1.000000|1.000000|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|0.581994|1974950 / 170474840204 (0.001158%)|260741 / 311891 (83.600040%)|0.030387|0.043756|0.064341|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|0.025246|0.164000|0.999988|0.581994|0.999987|0.000013|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|0.030387|0.043756|0.078123|0.022367|0.064345|0.064341|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|311891|0|0|170474840204|0 / 170474840204|0 / 311891|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|51150|1974950|260741|170472865254|1974950 / 170474840204|260741 / 311891|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999966|11470018 / 170474840204 (0.006728%)|0 / 311891 (0.000000%)|0.032872|0.051579|0.162697|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|0.581994|2004598 / 170474840204 (0.001176%)|260741 / 311891 (83.600040%)|0.029965|0.043208|0.063875|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.026472|1.000000|0.999933|0.999966|0.999933|0.000067|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|0.024881|0.164000|0.999988|0.581994|0.999987|0.000013|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.032872|0.051579|0.119687|0.026472|0.162702|0.162697|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|0.029965|0.043208|0.077422|0.022081|0.063879|0.063875|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|311891|11470018|0|170463370186|11470018 / 170474840204|0 / 311891|
+|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|51150|2004598|260741|170472835606|2004598 / 170474840204|260741 / 311891|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|21319|1148489|10321529|28826|4.936720%|1355|2812871|
+|HUNSPELL ENGLISH LUCENE FILTER|5224|3091|26557|6786|1.162165%|4|590716|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `US_UK`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/finnish.md

@@ -26,7 +26,7 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
@@ -41,10 +41,10 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `finnishRadixor` | 228.248 | 9.245 | 130.1 | 1.000 | Full Radixor dictionary patch-command stemmer. |
+| Radixor | `finnishRadixor` | 308.076 | 15.529 | 175.6 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Lucene FinnishLightStemFilter | `finnishLuceneFinnishLightStemFilter` | 175.923 | 87.177 | 100.3 | 0.771 | Light Finnish suffix stemmer. |
+| Lucene FinnishLightStemFilter | `finnishLuceneFinnishLightStemFilter` | 175.250 | 46.995 | 99.9 | 0.569 | Light Finnish suffix stemmer. |
-| Official Snowball direct | `snowballDirect[FINNISH]` | 265.579 | 95.687 | 151.4 | 1.164 | Official Snowball generated Java stemmer; direct API. |
+| Official Snowball direct | `snowballDirect[FINNISH]` | 264.652 | 63.054 | 150.8 | 0.859 | Official Snowball generated Java stemmer; direct API. |
-| Lucene SnowballFilter | `luceneSnowballFilter[FINNISH]` | 338.099 | 175.101 | 192.7 | 1.481 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Lucene SnowballFilter | `luceneSnowballFilter[FINNISH]` | 374.883 | 238.157 | 213.6 | 1.217 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
 
 ## Interpretation Notes
 
@@ -53,3 +53,356 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `FI_FI` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/fi_fi/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.984594** among 4 deterministic stemmers. The runner-up is `SNOWBALL FINNISH LUCENE FILTER` at 0.740353, a difference of 0.244242. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.988068** among 4 deterministic stemmers. The runner-up is `SNOWBALL FINNISH DIRECT` at 0.738400, a difference of 0.249668. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.984594|731279 / 1641126814491 (0.000045%)|971268 / 31523695 (3.081073%)|0.975128|0.972893|0.972899|
+|2|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.740353|1922153 / 1641126814491 (0.000117%)|16370057 / 31523695 (51.929372%)|0.758996|0.623613|0.653138|
+|3|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.739729|1544812 / 1641126814491 (0.000094%)|16409363 / 31523695 (52.054060%)|0.769880|0.627374|0.659540|
+|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.695969|2223150 / 1641126814491 (0.000135%)|19168306 / 31523695 (60.806025%)|0.687649|0.536000|0.576338|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.976624|0.969189|1.000000|0.984594|0.999999|0.000001|
+|2|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.887434|0.480706|0.999999|0.740353|0.999989|0.000011|
+|3|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.907269|0.479459|0.999999|0.739729|0.999989|0.000011|
+|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.847505|0.391940|0.999999|0.695969|0.999987|0.000013|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.975128|0.972893|0.970667|0.947216|0.972900|0.972899|
+|2|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.758996|0.623613|0.529216|0.453080|0.653142|0.653138|
+|3|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.769880|0.627374|0.529384|0.457061|0.659544|0.659540|
+|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.687649|0.536000|0.439152|0.366120|0.576343|0.576338|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.972892|0.996085|0.993746|0.994914|0.994914|
+|2|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.623608|0.990718|0.904385|0.945585|0.945585|
+|3|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.627369|0.991872|0.904139|0.945975|0.945975|
+|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.535994|0.988126|0.886473|0.934544|0.934544|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|30552427|731279|971268|1641126083212|731279 / 1641126814491|971268 / 31523695|
+|2|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|15153638|1922153|16370057|1641124892338|1922153 / 1641126814491|16370057 / 31523695|
+|3|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|15114332|1544812|16409363|1641125269679|1544812 / 1641126814491|16409363 / 31523695|
+|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|12355389|2223150|19168306|1641124591341|2223150 / 1641126814491|19168306 / 31523695|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 1641126814491 (0.000000%)|0 / 31523695 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|31523695|0|0|1641126814491|0 / 1641126814491|0 / 31523695|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999999|1683575 / 1641126814491 (0.000103%)|0 / 31523695 (0.000000%)|0.959025|0.973991|0.974320|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.949301|1.000000|0.999999|0.999999|0.999999|0.000001|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.959025|0.973991|0.989432|0.949301|0.974321|0.974320|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|31523695|1683575|0|1641125130916|1683575 / 1641126814491|0 / 31523695|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|971268|731279|952296|57328|3.164291%|6|1876272|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.988068|730145 / 1543589444152 (0.000047%)|735305 / 30813833 (2.386282%)|0.976268|0.976219|0.976218|
+|2|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.738400|1513705 / 1543589444152 (0.000098%)|16121763 / 30813833 (52.319888%)|0.768117|0.624934|0.657464|
+|3|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.738400|1513705 / 1543589444152 (0.000098%)|16121763 / 30813833 (52.319888%)|0.768117|0.624934|0.657464|
+|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.694529|1806392 / 1543589444152 (0.000117%)|18825444 / 30813833 (61.094133%)|0.697056|0.537492|0.581469|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.976301|0.976137|1.000000|0.988068|0.999999|0.000001|
+|2|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.906595|0.476801|0.999999|0.738400|0.999989|0.000011|
+|3|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.906595|0.476801|0.999999|0.738400|0.999989|0.000011|
+|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.869053|0.389059|0.999999|0.694529|0.999987|0.000013|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.976268|0.976219|0.976170|0.953543|0.976219|0.976218|
+|2|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.768117|0.624934|0.526744|0.454475|0.657469|0.657464|
+|3|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.768117|0.624934|0.526744|0.454475|0.657469|0.657464|
+|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.697056|0.537492|0.437372|0.367514|0.581474|0.581469|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.976218|0.996000|0.996069|0.996035|0.996035|
+|2|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.624929|0.991732|0.902933|0.945252|0.945252|
+|3|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.624929|0.991732|0.902933|0.945252|0.945252|
+|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.537486|0.989268|0.885294|0.934397|0.934397|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|30078528|730145|735305|1543588714007|730145 / 1543589444152|735305 / 30813833|
+|2|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|14692070|1513705|16121763|1543587930447|1513705 / 1543589444152|16121763 / 30813833|
+|3|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|14692070|1513705|16121763|1543587930447|1513705 / 1543589444152|16121763 / 30813833|
+|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|11988389|1806392|18825444|1543587637760|1806392 / 1543589444152|18825444 / 30813833|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 1543589444152 (0.000000%)|0 / 30813833 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|30813833|0|0|1543589444152|0 / 1543589444152|0 / 30813833|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999999|1653320 / 1543589444152 (0.000107%)|0 / 30813833 (0.000000%)|0.958843|0.973873|0.974205|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.949077|1.000000|0.999999|0.999999|0.999999|0.000001|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.958843|0.973873|0.989383|0.949077|0.974206|0.974205|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|30813833|1653320|0|1543587790832|1653320 / 1543589444152|0 / 30813833|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|735305|730145|923175|44331|2.523029%|6|1805864|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `FI_FI`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/french.md

@@ -26,11 +26,12 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
 | Radixor | 94.831% | 94.859% | 94.734% | Full Radixor dictionary patch-command stemmer. |
+| Lucene HunspellStemFilter | 68.923% | 63.617% | 86.876% | Benchmark-only French Hunspell dictionary compared via Lucene HunspellStemFilter. |
 | Lucene FrenchMinimalStemFilter | 11.472% | 6.236% | 29.192% | Minimal suffix reducer; narrow baseline, not a full stemmer. |
 | Lucene SnowballFilter | 8.551% | 5.183% | 19.952% | Lucene TokenFilter integration path around the Snowball algorithm. |
 | Official Snowball direct | 8.462% | 5.067% | 19.952% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
@@ -42,11 +43,12 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `frenchRadixor` | 38.598 | 5.425 | 105.5 | 1.000 | Full Radixor dictionary patch-command stemmer. |
+| Radixor | `frenchRadixor` | 47.033 | 4.146 | 128.5 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Lucene FrenchMinimalStemFilter | `frenchLuceneFrenchMinimalStemFilter` | 17.657 | 1.956 | 48.2 | 0.457 | Minimal French suffix reducer; narrow baseline. |
+| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 1664.935 | 65.928 | 4549.4 | 35.399 | Benchmark-only French Hunspell dictionary compared via Lucene HunspellStemFilter. |
-| Lucene FrenchLightStemFilter | `frenchLuceneFrenchLightStemFilter` | 28.742 | 2.391 | 78.5 | 0.745 | Light French suffix stemmer. |
+| Lucene FrenchMinimalStemFilter | `frenchLuceneFrenchMinimalStemFilter` | 19.234 | 2.098 | 52.6 | 0.409 | Minimal French suffix reducer; narrow baseline. |
-| Official Snowball direct | `snowballDirect[FRENCH]` | 104.983 | 10.116 | 286.9 | 2.720 | Official Snowball generated Java stemmer; direct API. |
+| Lucene FrenchLightStemFilter | `frenchLuceneFrenchLightStemFilter` | 30.560 | 3.680 | 83.5 | 0.650 | Light French suffix stemmer. |
-| Lucene SnowballFilter | `luceneSnowballFilter[FRENCH]` | 117.938 | 4.007 | 322.3 | 3.056 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Official Snowball direct | `snowballDirect[FRENCH]` | 111.057 | 8.172 | 303.5 | 2.361 | Official Snowball generated Java stemmer; direct API. |
+| Lucene SnowballFilter | `luceneSnowballFilter[FRENCH]` | 123.648 | 3.500 | 337.9 | 2.629 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
 
 ## Interpretation Notes
 
@@ -55,3 +57,398 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `FR_FR` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/fr_fr/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.956992** among 6 deterministic stemmers. The runner-up is `SNOWBALL FRENCH DIRECT` at 0.845262, a difference of 0.111731. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.957224** among 6 deterministic stemmers. The runner-up is `SNOWBALL FRENCH DIRECT` at 0.845414, a difference of 0.111810. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **10 result rows**, **6 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.956992|318767 / 90396104830 (0.000353%)|469160 / 5454615 (8.601157%)|0.934603|0.926765|0.926851|
+|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.845262|1654723 / 90396104830 (0.001831%)|1687975 / 5454615 (30.945814%)|0.693926|0.692653|0.692638|
+|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.844999|1661388 / 90396104830 (0.001838%)|1690838 / 5454615 (30.998301%)|0.693010|0.691885|0.691869|
+|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.813742|776728 / 90396104830 (0.000859%)|2031881 / 5454615 (37.250677%)|0.769069|0.709075|0.715131|
+|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.518587|276403 / 90396104830 (0.000306%)|5251833 / 5454615 (96.282377%)|0.137547|0.068348|0.125415|
+|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.516830|160438 / 90396104830 (0.000177%)|5271003 / 5454615 (96.633823%)|0.134400|0.063329|0.134021|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.939903|0.913988|0.999996|0.956992|0.999991|0.000009|
+|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.694777|0.690542|0.999982|0.845262|0.999963|0.000037|
+|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.693763|0.690017|0.999982|0.844999|0.999963|0.000037|
+|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.815041|0.627493|0.999991|0.813742|0.999969|0.000031|
+|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.423181|0.037176|0.999997|0.518587|0.999939|0.000061|
+|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.533678|0.033662|0.999998|0.516830|0.999940|0.000060|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.934603|0.926765|0.919056|0.863524|0.926855|0.926851|
+|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.693926|0.692653|0.691385|0.529816|0.692656|0.692638|
+|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.693010|0.691885|0.690763|0.528917|0.691887|0.691869|
+|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.769069|0.709075|0.657765|0.549277|0.715145|0.715131|
+|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.137547|0.068348|0.045472|0.035383|0.125428|0.125415|
+|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.134400|0.063329|0.041424|0.032700|0.134032|0.134021|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.926760|0.988772|0.985214|0.986990|0.986990|
+|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.692635|0.959459|0.944948|0.952148|0.952148|
+|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.691866|0.958698|0.944715|0.951655|0.951655|
+|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.709060|0.978337|0.913706|0.944918|0.944918|
+|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.068339|0.974110|0.812376|0.885922|0.885922|
+|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.063322|0.984019|0.810979|0.889158|0.889158|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|4985455|318767|469160|90395786063|318767 / 90396104830|469160 / 5454615|
+|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|3766640|1654723|1687975|90394450107|1654723 / 90396104830|1687975 / 5454615|
+|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|3763777|1661388|1690838|90394443442|1661388 / 90396104830|1690838 / 5454615|
+|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|3422734|776728|2031881|90395328102|776728 / 90396104830|2031881 / 5454615|
+|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|202782|276403|5251833|90395828427|276403 / 90396104830|5251833 / 5454615|
+|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|183612|160438|5271003|90395944392|160438 / 90396104830|5271003 / 5454615|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|0.999979|12 / 90396104830 (0.000000%)|232 / 5454615 (0.004253%)|0.999990|0.999978|0.999978|
+|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|0.830964|745831 / 90396104830 (0.000825%)|1844003 / 5454615 (33.806291%)|0.789019|0.736029|0.740670|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|0.999998|0.999957|1.000000|0.999979|1.000000|0.000000|
+|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|0.828798|0.661937|0.999992|0.830964|0.999971|0.000029|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|0.999990|0.999978|0.999966|0.999955|0.999978|0.999978|
+|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|0.789019|0.736029|0.689709|0.582315|0.740684|0.740670|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|5454383|12|232|90396104818|12 / 90396104830|232 / 5454615|
+|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|3610612|745831|1844003|90395358999|745831 / 90396104830|1844003 / 5454615|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999973|1056255 / 90396104830 (0.001168%)|232 / 5454615 (0.004253%)|0.865853|0.911704|0.915270|
+|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|0.830963|1043199 / 90396104830 (0.001154%)|1844003 / 5454615 (33.806291%)|0.750028|0.714377|0.716613|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.837765|0.999957|0.999988|0.999973|0.999988|0.000012|
+|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|0.775840|0.661937|0.999988|0.830963|0.999968|0.000032|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.865853|0.911704|0.962682|0.837735|0.915275|0.915270|
+|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|0.750028|0.714377|0.681961|0.555666|0.716629|0.716613|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|5454383|1056255|232|90395048575|1056255 / 90396104830|232 / 5454615|
+|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|3610612|1043199|1844003|90395061631|1043199 / 90396104830|1844003 / 5454615|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|468928|318755|737488|43040|10.122057%|56|477024|
+|HUNSPELL FRENCH LUCENE FILTER|187878|30897|266471|13511|3.177489%|4|439015|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **10 result rows**, **6 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.957224|315266 / 88712126506 (0.000355%)|465436 / 5440559 (8.554930%)|0.935099|0.927248|0.927334|
+|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.845414|1646111 / 88712126506 (0.001856%)|1681970 / 5440559 (30.915389%)|0.694508|0.693130|0.693115|
+|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.845163|1641925 / 88712126506 (0.001851%)|1684703 / 5440559 (30.965623%)|0.694714|0.693068|0.693055|
+|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.813617|763305 / 88712126506 (0.000860%)|2028011 / 5440559 (37.275784%)|0.770537|0.709734|0.715938|
+|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.518442|262689 / 88712126506 (0.000296%)|5239869 / 5440559 (96.311225%)|0.137571|0.067985|0.126383|
+|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.516697|147476 / 88712126506 (0.000166%)|5258873 / 5440559 (96.660527%)|0.134439|0.062979|0.135757|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.940408|0.914451|0.999996|0.957224|0.999991|0.000009|
+|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.695430|0.690846|0.999981|0.845414|0.999962|0.000038|
+|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.695815|0.690344|0.999981|0.845163|0.999963|0.000037|
+|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.817210|0.627242|0.999991|0.813617|0.999969|0.000031|
+|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.433101|0.036888|0.999997|0.518442|0.999938|0.000062|
+|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.551965|0.033395|0.999998|0.516697|0.999939|0.000061|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.935099|0.927248|0.919527|0.864363|0.927338|0.927334|
+|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.694508|0.693130|0.691758|0.530374|0.693134|0.693115|
+|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.694714|0.693068|0.691431|0.530302|0.693074|0.693055|
+|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.770537|0.709734|0.657826|0.550068|0.715953|0.715938|
+|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.137571|0.067985|0.045148|0.035189|0.126397|0.126383|
+|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.134439|0.062979|0.041121|0.032513|0.135767|0.135757|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.927243|0.988916|0.985550|0.987230|0.987230|
+|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.693112|0.959521|0.944537|0.951970|0.951970|
+|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.693050|0.959566|0.944385|0.951915|0.951915|
+|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.709719|0.979328|0.913162|0.945088|0.945088|
+|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.067976|0.975086|0.811144|0.885591|0.885591|
+|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.062973|0.985086|0.809774|0.888868|0.888868|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|4975123|315266|465436|88711811240|315266 / 88712126506|465436 / 5440559|
+|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|3758589|1646111|1681970|88710480395|1646111 / 88712126506|1681970 / 5440559|
+|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|3755856|1641925|1684703|88710484581|1641925 / 88712126506|1684703 / 5440559|
+|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|3412548|763305|2028011|88711363201|763305 / 88712126506|2028011 / 5440559|
+|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|200690|262689|5239869|88711863817|262689 / 88712126506|5239869 / 5440559|
+|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|181686|147476|5258873|88711979030|147476 / 88712126506|5258873 / 5440559|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 88712126506 (0.000000%)|0 / 5440559 (0.000000%)|1.000000|1.000000|1.000000|
+|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|0.830852|733584 / 88712126506 (0.000827%)|1840476 / 5440559 (33.828803%)|0.790351|0.736648|0.741404|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|0.830724|0.661712|0.999992|0.830852|0.999971|0.000029|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|0.790351|0.736648|0.689779|0.583090|0.741418|0.741404|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|5440559|0|0|88712126506|0 / 88712126506|0 / 5440559|
+|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|3600083|733584|1840476|88711392922|733584 / 88712126506|1840476 / 5440559|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999995|938985 / 88712126506 (0.001058%)|0 / 5440559 (0.000000%)|0.878679|0.920560|0.923474|
+|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|0.830850|1027635 / 88712126506 (0.001158%)|1840476 / 5440559 (33.828803%)|0.751538|0.715134|0.717460|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.852813|1.000000|0.999989|0.999995|0.999989|0.000011|
+|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|0.777939|0.661712|0.999988|0.830850|0.999968|0.000032|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.878679|0.920560|0.966634|0.852813|0.923479|0.923474|
+|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|0.751538|0.715134|0.682093|0.556582|0.717476|0.717460|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|5440559|938985|0|88711187521|938985 / 88712126506|0 / 5440559|
+|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|3600083|1027635|1840476|88711098871|1027635 / 88712126506|1840476 / 5440559|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|465436|315266|623719|41130|9.764239%|56|468574|
+|HUNSPELL FRENCH LUCENE FILTER|187535|29721|264330|13437|3.189936%|4|434961|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `FR_FR`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/german.md

@@ -26,16 +26,18 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
-| Radixor | 97.455% | 97.973% | 96.476% | Full Radixor dictionary patch-command stemmer. |
+| Radixor | 92.725% | 92.847% | 92.396% | Full Radixor dictionary patch-command stemmer. |
-| Lucene GermanLightStemFilter | 38.583% | 35.800% | 43.849% | Light suffix stemmer; intentionally narrower than a dictionary-derived stemmer. |
+| Lucene HunspellStemFilter | 47.064% | 29.661% | 93.678% | Benchmark-only German Hunspell dictionary compared via Lucene HunspellStemFilter. |
-| Lucene GermanMinimalStemFilter | 37.492% | 38.538% | 35.513% | Minimal suffix reducer; narrow baseline, not a full stemmer. |
+| CISTEM (German) | 24.675% | 23.724% | 27.222% | Benchmark-only CISTEM implementation. |
-| Lucene SnowballFilter | 33.380% | 30.939% | 37.999% | Lucene TokenFilter integration path around the Snowball algorithm. |
+| Lucene GermanLightStemFilter | 37.434% | 35.465% | 42.707% | Light suffix stemmer; intentionally narrower than a dictionary-derived stemmer. |
-| Official Snowball direct | 32.863% | 31.225% | 35.963% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
+| Lucene GermanMinimalStemFilter | 27.640% | 24.951% | 34.844% | Minimal suffix reducer; narrow baseline, not a full stemmer. |
-| Lucene GermanStemFilter | 26.168% | 24.979% | 28.416% | German Lucene stemming TokenFilter; broader than minimal/light variants. |
+| Lucene SnowballFilter | 30.956% | 28.853% | 36.589% | Lucene TokenFilter integration path around the Snowball algorithm. |
+| Official Snowball direct | 30.481% | 29.027% | 34.376% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
+| Lucene GermanStemFilter | 21.559% | 19.312% | 27.576% | German Lucene stemming TokenFilter; broader than minimal/light variants. |
 
 ## Speed
 
@@ -43,12 +45,14 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `germanRadixor` | 9.518 | 0.338 | 68.2 | 1.000 | Full Radixor dictionary patch-command stemmer. |
+| Radixor | `germanRadixor` | 41.166 | 2.396 | 294.8 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Lucene GermanMinimalStemFilter | `germanLuceneGermanMinimalStemFilter` | 12.020 | 0.460 | 86.1 | 1.263 | Minimal German suffix reduction; narrow baseline. |
+| CISTEM | `germanCistem` | 248.392 | 12.294 | 1778.8 | 6.034 | Benchmark-only CISTEM implementation. |
-| Lucene GermanLightStemFilter | `germanLuceneGermanLightStemFilter` | 12.413 | 1.059 | 88.9 | 1.304 | Light German suffix stemmer; narrower than a dictionary stemmer. |
+| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 281.322 | 3.411 | 2014.6 | 6.834 | Benchmark-only German Hunspell dictionary compared via Lucene HunspellStemFilter. |
-| Lucene GermanStemFilter | `germanLuceneGermanStemFilter` | 36.644 | 5.667 | 262.4 | 3.850 | Older German stemming TokenFilter with normalization requirements. |
+| Lucene GermanMinimalStemFilter | `germanLuceneGermanMinimalStemFilter` | 23.562 | 0.969 | 168.7 | 0.572 | Minimal German suffix reduction; narrow baseline. |
-| Lucene SnowballFilter | `luceneSnowballFilter[GERMAN]` | 54.846 | 8.710 | 392.8 | 5.762 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Lucene GermanLightStemFilter | `germanLuceneGermanLightStemFilter` | 24.410 | 1.034 | 174.8 | 0.593 | Light German suffix stemmer; narrower than a dictionary stemmer. |
-| Official Snowball direct | `snowballDirect[GERMAN]` | 52.974 | 7.989 | 379.4 | 5.566 | Official Snowball generated Java stemmer; direct API. |
+| Lucene GermanStemFilter | `germanLuceneGermanStemFilter` | 71.039 | 4.443 | 508.7 | 1.726 | Older German stemming TokenFilter with normalization requirements. |
+| Lucene SnowballFilter | `luceneSnowballFilter[GERMAN]` | 105.771 | 9.617 | 757.4 | 2.569 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Official Snowball direct | `snowballDirect[GERMAN]` | 100.688 | 9.018 | 721.0 | 2.446 | Official Snowball generated Java stemmer; direct API. |
 
 ## Interpretation Notes
 
@@ -57,3 +61,418 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `DE_DE` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/de_de/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.907901** among 8 deterministic stemmers. The runner-up is `GERMAN CISTEM` at 0.880770, a difference of 0.027131. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.966157** among 8 deterministic stemmers. The runner-up is `GERMAN CISTEM` at 0.915288, a difference of 0.050869. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **12 result rows**, **8 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.907901|98192 / 44095245979 (0.000223%)|254903 / 1383872 (18.419550%)|0.897073|0.864768|0.866326|
+|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.880770|477122 / 44095245979 (0.001082%)|329983 / 1383872 (23.844908%)|0.701852|0.723109|0.724023|
+|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.778614|190680 / 44095245979 (0.000432%)|612734 / 1383872 (44.276783%)|0.737064|0.657494|0.668394|
+|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.771357|295701 / 44095245979 (0.000671%)|632816 / 1383872 (45.727929%)|0.674089|0.617993|0.624014|
+|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.756258|205740 / 44095245979 (0.000467%)|674609 / 1383872 (48.747933%)|0.703092|0.617052|0.630292|
+|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.723772|331871 / 44095245979 (0.000753%)|764518 / 1383872 (55.244849%)|0.596821|0.530474|0.539809|
+|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.641579|203883 / 44095245979 (0.000462%)|992010 / 1383872 (71.683653%)|0.520145|0.395897|0.431563|
+|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.598139|110840 / 44095245979 (0.000251%)|1112246 / 1383872 (80.372029%)|0.466113|0.307558|0.373350|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.919984|0.815804|0.999998|0.907901|0.999992|0.000008|
+|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.688361|0.761551|0.999989|0.880770|0.999982|0.000018|
+|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.801750|0.557232|0.999996|0.778614|0.999982|0.000018|
+|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.717508|0.542721|0.999993|0.771357|0.999979|0.000021|
+|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.775148|0.512521|0.999995|0.756258|0.999980|0.000020|
+|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.651112|0.447552|0.999992|0.723772|0.999975|0.000025|
+|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.657768|0.283163|0.999995|0.641579|0.999973|0.000027|
+|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.710196|0.196280|0.999997|0.598139|0.999972|0.000028|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.897073|0.864768|0.834709|0.761755|0.866330|0.866326|
+|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.701852|0.723109|0.745694|0.566304|0.724032|0.724023|
+|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.737064|0.657494|0.593429|0.489751|0.668402|0.668394|
+|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.674089|0.617993|0.570517|0.447171|0.624024|0.624014|
+|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.703092|0.617052|0.549774|0.446186|0.630301|0.630292|
+|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.596821|0.530474|0.477402|0.360983|0.539820|0.539809|
+|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.520145|0.395897|0.319562|0.246803|0.431574|0.431563|
+|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.466113|0.307558|0.229493|0.181725|0.373359|0.373350|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.864764|0.989946|0.975085|0.982460|0.982460|
+|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.723100|0.974048|0.975147|0.974597|0.974597|
+|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.657485|0.983725|0.949324|0.966218|0.966218|
+|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.617983|0.975845|0.942925|0.959102|0.959102|
+|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.617043|0.980753|0.936533|0.958133|0.958133|
+|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.530462|0.975550|0.942890|0.958942|0.958942|
+|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.395885|0.980463|0.886873|0.931322|0.931322|
+|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.307549|0.983615|0.896264|0.937910|0.937910|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|1128969|98192|254903|44095147787|98192 / 44095245979|254903 / 1383872|
+|2|GERMAN CISTEM|PRIMARY_OUTPUT|1053889|477122|329983|44094768857|477122 / 44095245979|329983 / 1383872|
+|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|771138|190680|612734|44095055299|190680 / 44095245979|612734 / 1383872|
+|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|751056|295701|632816|44094950278|295701 / 44095245979|632816 / 1383872|
+|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|709263|205740|674609|44095040239|205740 / 44095245979|674609 / 1383872|
+|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|619354|331871|764518|44094914108|331871 / 44095245979|764518 / 1383872|
+|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|391862|203883|992010|44095042096|203883 / 44095245979|992010 / 1383872|
+|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|271626|110840|1112246|44095135139|110840 / 44095245979|1112246 / 1383872|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|0.959835|1375 / 44095245979 (0.000003%)|111167 / 1383872 (8.033041%)|0.981996|0.957658|0.958475|
+|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|0.647474|158403 / 44095245979 (0.000359%)|975697 / 1383872 (70.504859%)|0.559116|0.418544|0.460956|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|0.998921|0.919670|1.000000|0.959835|0.999997|0.000003|
+|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|0.720422|0.294951|0.999996|0.647474|0.999974|0.000026|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|0.981996|0.957658|0.934498|0.918757|0.958476|0.958475|
+|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|0.559116|0.418544|0.334456|0.264658|0.460966|0.460956|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1272705|1375|111167|44095244604|1375 / 44095245979|111167 / 1383872|
+|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|408175|158403|975697|44095087576|158403 / 44095245979|975697 / 1383872|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.959832|244817 / 44095245979 (0.000555%)|111167 / 1383872 (8.033041%)|0.853711|0.877306|0.878234|
+|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|0.647473|242551 / 44095245979 (0.000550%)|975697 / 1383872 (70.504859%)|0.511911|0.401234|0.430118|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.838673|0.919670|0.999994|0.959832|0.999992|0.000008|
+|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|0.627261|0.294951|0.999994|0.647473|0.999972|0.000028|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.853711|0.877306|0.902242|0.781429|0.878238|0.878234|
+|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|0.511911|0.401234|0.329907|0.250965|0.430130|0.430118|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|1272705|244817|111167|44095001162|244817 / 44095245979|111167 / 1383872|
+|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|408175|242551|975697|44095003428|242551 / 44095245979|975697 / 1383872|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|143736|96817|146625|48574|16.356314%|8|361016|
+|HUNSPELL GERMAN LUCENE FILTER|16313|45480|38668|7891|2.657135%|3|305052|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **12 result rows**, **8 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.966157|47898 / 11263756342 (0.000425%)|59114 / 873411 (6.768177%)|0.941996|0.938343|0.938358|
+|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.915288|156784 / 11263756342 (0.001392%)|147964 / 873411 (16.940936%)|0.823934|0.826418|0.826415|
+|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.795926|87697 / 11263756342 (0.000779%)|356475 / 873411 (40.814118%)|0.785153|0.699487|0.711329|
+|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.775641|77653 / 11263756342 (0.000689%)|391910 / 873411 (44.871200%)|0.774111|0.672222|0.688986|
+|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.769953|55477 / 11263756342 (0.000493%)|401846 / 873411 (46.008809%)|0.790797|0.673446|0.695023|
+|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.716810|78723 / 11263756342 (0.000699%)|494677 / 873411 (56.637368%)|0.700519|0.569153|0.599149|
+|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.659196|84679 / 11263756342 (0.000752%)|595318 / 873411 (68.160122%)|0.598178|0.449922|0.494019|
+|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.575691|21214 / 11263756342 (0.000188%)|741190 / 873411 (84.861537%)|0.444545|0.257528|0.361168|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.944446|0.932318|0.999996|0.966157|0.999991|0.000009|
+|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.822287|0.830591|0.999986|0.915288|0.999973|0.000027|
+|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.854958|0.591859|0.999992|0.795926|0.999961|0.000039|
+|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.861124|0.551288|0.999993|0.775641|0.999958|0.000042|
+|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.894739|0.539912|0.999995|0.769953|0.999959|0.000041|
+|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.827912|0.433626|0.999993|0.716810|0.999949|0.000051|
+|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.766578|0.318399|0.999992|0.659196|0.999940|0.000060|
+|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.861739|0.151385|0.999998|0.575691|0.999932|0.000068|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.941996|0.938343|0.934719|0.883848|0.938363|0.938358|
+|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.823934|0.826418|0.828917|0.704184|0.826428|0.826415|
+|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.785153|0.699487|0.630675|0.537854|0.711347|0.711329|
+|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.774111|0.672222|0.594035|0.506276|0.689005|0.688986|
+|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.790797|0.673446|0.586424|0.507666|0.695040|0.695023|
+|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.700519|0.569153|0.479277|0.397774|0.599170|0.599149|
+|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.598178|0.449922|0.360559|0.290258|0.494042|0.494019|
+|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.444545|0.257528|0.181270|0.147795|0.361184|0.361168|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.938338|0.994062|0.990664|0.992360|0.992360|
+|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.826404|0.985936|0.973570|0.979714|0.979714|
+|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.699468|0.988418|0.932452|0.959619|0.959619|
+|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.672202|0.989021|0.919542|0.953017|0.953017|
+|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.673427|0.991320|0.915070|0.951670|0.951670|
+|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.569130|0.988584|0.918718|0.952371|0.952371|
+|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.449897|0.988041|0.865581|0.922766|0.922766|
+|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.257511|0.992643|0.854403|0.918349|0.918349|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|814297|47898|59114|11263708444|47898 / 11263756342|59114 / 873411|
+|2|GERMAN CISTEM|PRIMARY_OUTPUT|725447|156784|147964|11263599558|156784 / 11263756342|147964 / 873411|
+|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|516936|87697|356475|11263668645|87697 / 11263756342|356475 / 873411|
+|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|481501|77653|391910|11263678689|77653 / 11263756342|391910 / 873411|
+|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|471565|55477|401846|11263700865|55477 / 11263756342|401846 / 873411|
+|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|378734|78723|494677|11263677619|78723 / 11263756342|494677 / 873411|
+|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|278093|84679|595318|11263671663|84679 / 11263756342|595318 / 873411|
+|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|132221|21214|741190|11263735128|21214 / 11263756342|741190 / 873411|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 11263756342 (0.000000%)|0 / 873411 (0.000000%)|1.000000|1.000000|1.000000|
+|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|0.665363|60996 / 11263756342 (0.000542%)|584547 / 873411 (66.926911%)|0.635466|0.472281|0.522540|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|0.825656|0.330731|0.999995|0.665363|0.999943|0.000057|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|0.635466|0.472281|0.375782|0.309142|0.522561|0.522540|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|873411|0|0|11263756342|0 / 11263756342|0 / 873411|
+|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|288864|60996|584547|11263695346|60996 / 11263756342|584547 / 873411|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999996|97544 / 11263756342 (0.000866%)|0 / 873411 (0.000000%)|0.917983|0.947112|0.948436|
+|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|0.665361|96545 / 11263756342 (0.000857%)|584547 / 873411 (66.926911%)|0.598050|0.458944|0.497855|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.899538|1.000000|0.999991|0.999996|0.999991|0.000009|
+|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|0.749500|0.330731|0.999991|0.665361|0.999940|0.000060|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.917983|0.947112|0.978152|0.899538|0.948440|0.948436|
+|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|0.598050|0.458944|0.372338|0.297811|0.497878|0.497855|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|873411|97544|0|11263658798|97544 / 11263756342|0 / 873411|
+|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|288864|96545|584547|11263659797|96545 / 11263756342|584547 / 873411|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|59114|47898|49646|14978|9.978814%|8|167157|
+|HUNSPELL GERMAN LUCENE FILTER|10771|23683|11866|4989|3.323828%|3|155207|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `DE_DE`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/hungarian.md

@@ -26,7 +26,7 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
@@ -41,10 +41,10 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `hungarianRadixor` | 53.844 | 5.619 | 60.0 | 1.000 | Full Radixor dictionary patch-command stemmer. |
+| Radixor | `hungarianRadixor` | 62.232 | 6.412 | 69.4 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Lucene HungarianLightStemFilter | `hungarianLuceneHungarianLightStemFilter` | 85.802 | 4.554 | 95.7 | 1.594 | Light Hungarian suffix stemmer. |
+| Lucene HungarianLightStemFilter | `hungarianLuceneHungarianLightStemFilter` | 92.813 | 6.929 | 103.5 | 1.491 | Light Hungarian suffix stemmer. |
-| Official Snowball direct | `snowballDirect[HUNGARIAN]` | 161.996 | 61.038 | 180.6 | 3.009 | Official Snowball generated Java stemmer; direct API. |
+| Official Snowball direct | `snowballDirect[HUNGARIAN]` | 157.765 | 13.202 | 175.9 | 2.535 | Official Snowball generated Java stemmer; direct API. |
-| Lucene SnowballFilter | `luceneSnowballFilter[HUNGARIAN]` | 185.097 | 44.447 | 206.4 | 3.438 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Lucene SnowballFilter | `luceneSnowballFilter[HUNGARIAN]` | 188.863 | 15.880 | 210.6 | 3.035 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
 
 ## Interpretation Notes
 
@@ -53,3 +53,356 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `HU_HU` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/hu_hu/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.995491** among 4 deterministic stemmers. The runner-up is `SNOWBALL HUNGARIAN LUCENE FILTER` at 0.822606, a difference of 0.172885. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.996163** among 4 deterministic stemmers. The runner-up is `SNOWBALL HUNGARIAN DIRECT` at 0.821708, a difference of 0.174455. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.995491|272900 / 419820542893 (0.000065%)|199837 / 22162103 (0.901706%)|0.988376|0.989352|0.989353|
+|2|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.822606|1792049 / 419820542893 (0.000427%)|7862745 / 22162103 (35.478334%)|0.826288|0.747610|0.757196|
+|3|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.822348|1506056 / 419820542893 (0.000359%)|7874191 / 22162103 (35.529981%)|0.837137|0.752866|0.763681|
+|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.816668|4132555 / 419820542893 (0.000984%)|8125833 / 22162103 (36.665442%)|0.740018|0.696055|0.699478|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.987727|0.990983|0.999999|0.995491|0.999999|0.000001|
+|2|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.888633|0.645217|0.999996|0.822606|0.999977|0.000023|
+|3|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.904644|0.644700|0.999996|0.822348|0.999978|0.000022|
+|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.772547|0.633346|0.999990|0.816668|0.999971|0.000029|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.988376|0.989352|0.990330|0.978929|0.989353|0.989353|
+|2|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.826288|0.747610|0.682613|0.596947|0.757206|0.757196|
+|3|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.837137|0.752866|0.684009|0.603677|0.763691|0.763681|
+|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.740018|0.696055|0.657023|0.533807|0.699492|0.699478|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.989352|0.998036|0.997809|0.997922|0.997922|
+|2|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.747599|0.990687|0.924490|0.956445|0.956445|
+|3|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.752855|0.991948|0.924304|0.956932|0.956932|
+|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.696040|0.982615|0.926772|0.953877|0.953877|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|21962266|272900|199837|419820269993|272900 / 419820542893|199837 / 22162103|
+|2|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|14299358|1792049|7862745|419818750844|1792049 / 419820542893|7862745 / 22162103|
+|3|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|14287912|1506056|7874191|419819036837|1506056 / 419820542893|7874191 / 22162103|
+|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|14036270|4132555|8125833|419816410338|4132555 / 419820542893|8125833 / 22162103|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 419820542893 (0.000000%)|0 / 22162103 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|22162103|0|0|419820542893|0 / 419820542893|0 / 22162103|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999999|460158 / 419820542893 (0.000110%)|0 / 22162103 (0.000000%)|0.983661|0.989725|0.989777|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.979659|1.000000|0.999999|0.999999|0.999999|0.000001|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.983661|0.989725|0.995865|0.979659|0.989777|0.989777|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|22162103|460158|0|419820082735|460158 / 419820542893|0 / 22162103|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|199837|272900|187258|12320|1.344473%|5|929326|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.996163|272775 / 385870694917 (0.000071%)|164277 / 21411411 (0.767240%)|0.988321|0.989820|0.989822|
+|2|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.821708|1496670 / 385870694917 (0.000388%)|7634885 / 21411411 (35.658019%)|0.834899|0.751079|0.761809|
+|3|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.821708|1496670 / 385870694917 (0.000388%)|7634885 / 21411411 (35.658019%)|0.834899|0.751079|0.761809|
+|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.815077|3639046 / 385870694917 (0.000943%)|7918708 / 21411411 (36.983588%)|0.750108|0.700135|0.704477|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.987325|0.992328|0.999999|0.996163|0.999999|0.000001|
+|2|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.902007|0.643420|0.999996|0.821708|0.999976|0.000024|
+|3|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.902007|0.643420|0.999996|0.821708|0.999976|0.000024|
+|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.787585|0.630164|0.999991|0.815077|0.999970|0.000030|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.988321|0.989820|0.991323|0.979845|0.989823|0.989822|
+|2|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.834899|0.751079|0.682555|0.601383|0.761820|0.761809|
+|3|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.834899|0.751079|0.682555|0.601383|0.761820|0.761809|
+|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.750108|0.700135|0.656404|0.538621|0.704491|0.704477|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.989819|0.997945|0.998273|0.998109|0.998109|
+|2|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.751068|0.991610|0.923288|0.956230|0.956230|
+|3|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.751068|0.991610|0.923288|0.956230|0.956230|
+|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.700120|0.983687|0.925487|0.953700|0.953700|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|21247134|272775|164277|385870422142|272775 / 385870694917|164277 / 21411411|
+|2|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|13776526|1496670|7634885|385869198247|1496670 / 385870694917|7634885 / 21411411|
+|3|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|13776526|1496670|7634885|385869198247|1496670 / 385870694917|7634885 / 21411411|
+|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|13492703|3639046|7918708|385867055871|3639046 / 385870694917|7918708 / 21411411|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 385870694917 (0.000000%)|0 / 21411411 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|21411411|0|0|385870694917|0 / 385870694917|0 / 21411411|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999999|458462 / 385870694917 (0.000119%)|0 / 21411411 (0.000000%)|0.983159|0.989407|0.989462|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.979037|1.000000|0.999999|0.999999|0.999999|0.000001|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.983159|0.989407|0.995736|0.979037|0.989463|0.989462|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|21411411|458462|0|385870236455|458462 / 385870694917|0 / 21411411|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|164277|272775|185687|11153|1.269532%|5|890245|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `HU_HU`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/index.md

@@ -1,12 +1,12 @@
 # Language Benchmark Pages
 
-This section splits Radixor stemmer benchmark results by language. Each language page lists accuracy first and speed second.
+This section splits Radixor stemmer benchmark results by language. Each language page preserves the existing exact-root accuracy and runtime-performance results and adds pairwise stemming-quality tables for both dictionary-processing modes.
 
 ## Reference Pages
 
 | Page | Purpose |
 | --- | --- |
-| [Methodology](../reference/methodology.md) | Workload design, normalization, speed metrics, and quality metrics. |
+| [Methodology](../reference/methodology.md) | Workload design, normalization, speed metrics, and exact-root quality metrics. Pairwise quality definitions are also reproduced on every language page. |
 | [Corpora](../reference/corpora.md) | Dictionary sizes and changed-token timing workloads. |
 | [Environment and reports](../reference/environment.md) | Hardware, JVM, JMH settings, report files, and badge policy. |
 | [English dictionary coverage](../reference/english-coverage.md) | Quality/speed operating curve for contracted Radixor tries built from 100% down to 10% of English dictionary rows. |

docs/benchmarks/languages/italian.md

@@ -25,7 +25,7 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
@@ -40,10 +40,10 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `italianRadixor` | 23.776 | 9.977 | 74.9 | 1.000 | Full Radixor dictionary patch-command stemmer. |
+| Radixor | `italianRadixor` | 24.491 | 3.128 | 77.1 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Lucene ItalianLightStemFilter | `italianLuceneItalianLightStemFilter` | 14.940 | 1.682 | 47.1 | 0.628 | Light Italian suffix stemmer. |
+| Lucene ItalianLightStemFilter | `italianLuceneItalianLightStemFilter` | 15.977 | 1.041 | 50.3 | 0.652 | Light Italian suffix stemmer. |
-| Official Snowball direct | `snowballDirect[ITALIAN]` | 99.401 | 9.433 | 313.0 | 4.181 | Official Snowball generated Java stemmer; direct API. |
+| Official Snowball direct | `snowballDirect[ITALIAN]` | 109.526 | 12.572 | 344.9 | 4.472 | Official Snowball generated Java stemmer; direct API. |
-| Lucene SnowballFilter | `luceneSnowballFilter[ITALIAN]` | 108.462 | 8.014 | 341.6 | 4.562 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Lucene SnowballFilter | `luceneSnowballFilter[ITALIAN]` | 116.260 | 7.459 | 366.1 | 4.747 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
 
 ## Interpretation Notes
 
@@ -52,3 +52,356 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `IT_IT` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/it_it/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.996507** among 4 deterministic stemmers. The runner-up is `SNOWBALL ITALIAN DIRECT` at 0.866189, a difference of 0.130318. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.996512** among 4 deterministic stemmers. The runner-up is `SNOWBALL ITALIAN DIRECT` at 0.866205, a difference of 0.130307. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.996507|124172 / 53638521211 (0.000231%)|42908 / 6143814 (0.698394%)|0.982618|0.986492|0.986512|
+|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.866189|504775 / 53638521211 (0.000941%)|1644164 / 6143814 (26.761292%)|0.859975|0.807240|0.811470|
+|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.866189|504775 / 53638521211 (0.000941%)|1644164 / 6143814 (26.761292%)|0.859975|0.807240|0.811470|
+|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.508926|10589 / 53638521211 (0.000020%)|6034130 / 6143814 (98.214725%)|0.082782|0.035020|0.127588|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.980053|0.993016|0.999998|0.996507|0.999997|0.000003|
+|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.899134|0.732387|0.999991|0.866189|0.999960|0.000040|
+|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.899134|0.732387|0.999991|0.866189|0.999960|0.000040|
+|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.911959|0.017853|1.000000|0.508926|0.999887|0.000113|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.982618|0.986492|0.990396|0.973344|0.986513|0.986512|
+|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.859975|0.807240|0.760598|0.676783|0.811489|0.811470|
+|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.859975|0.807240|0.760598|0.676783|0.811489|0.811470|
+|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.082782|0.035020|0.022207|0.017822|0.127597|0.127588|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.986490|0.995780|0.997113|0.996446|0.996446|
+|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.807220|0.987994|0.933408|0.959925|0.959925|
+|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.807220|0.987994|0.933408|0.959925|0.959925|
+|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.035016|0.997481|0.737537|0.848037|0.848037|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|6100906|124172|42908|53638397039|124172 / 53638521211|42908 / 6143814|
+|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|4499650|504775|1644164|53638016436|504775 / 53638521211|1644164 / 6143814|
+|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|4499650|504775|1644164|53638016436|504775 / 53638521211|1644164 / 6143814|
+|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|109684|10589|6034130|53638510622|10589 / 53638521211|6034130 / 6143814|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|0.999993|0 / 53638521211 (0.000000%)|80 / 6143814 (0.001302%)|0.999997|0.999993|0.999993|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0.999987|1.000000|0.999993|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|0.999997|0.999993|0.999990|0.999987|0.999993|0.999993|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|6143734|0|80|53638521211|0 / 53638521211|80 / 6143814|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999992|170950 / 53638521211 (0.000319%)|80 / 6143814 (0.001302%)|0.978222|0.986272|0.986363|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.972928|0.999987|0.999997|0.999992|0.999997|0.000003|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.978222|0.986272|0.994455|0.972916|0.986365|0.986363|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|6143734|170950|80|53638350261|170950 / 53638521211|80 / 6143814|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|42828|124172|46778|6254|1.909321%|4|334175|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.996512|124171 / 53611667072 (0.000232%)|42828 / 6142174 (0.697278%)|0.982617|0.986495|0.986515|
+|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.866205|504774 / 53611667072 (0.000942%)|1643522 / 6142174 (26.757985%)|0.859970|0.807252|0.811479|
+|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.866205|504774 / 53611667072 (0.000942%)|1643522 / 6142174 (26.757985%)|0.859970|0.807252|0.811479|
+|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.508927|10588 / 53611667072 (0.000020%)|6032516 / 6142174 (98.214671%)|0.082784|0.035021|0.127589|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.980048|0.993027|0.999998|0.996512|0.999997|0.000003|
+|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.899114|0.732420|0.999991|0.866205|0.999960|0.000040|
+|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.899114|0.732420|0.999991|0.866205|0.999960|0.000040|
+|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.911947|0.017853|1.000000|0.508927|0.999887|0.000113|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.982617|0.986495|0.990404|0.973350|0.986516|0.986515|
+|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.859970|0.807252|0.760624|0.676800|0.811498|0.811479|
+|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.859970|0.807252|0.760624|0.676800|0.811498|0.811479|
+|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.082784|0.035021|0.022208|0.017823|0.127598|0.127589|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.986493|0.995780|0.997115|0.996447|0.996447|
+|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.807232|0.987991|0.933413|0.959927|0.959927|
+|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.807232|0.987991|0.933413|0.959927|0.959927|
+|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.035017|0.997481|0.737534|0.848035|0.848035|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|6099346|124171|42828|53611542901|124171 / 53611667072|42828 / 6142174|
+|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|4498652|504774|1643522|53611162298|504774 / 53611667072|1643522 / 6142174|
+|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|4498652|504774|1643522|53611162298|504774 / 53611667072|1643522 / 6142174|
+|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|109658|10588|6032516|53611656484|10588 / 53611667072|6032516 / 6142174|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 53611667072 (0.000000%)|0 / 6142174 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|6142174|0|0|53611667072|0 / 53611667072|0 / 6142174|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999998|170949 / 53611667072 (0.000319%)|0 / 6142174 (0.000000%)|0.978219|0.986275|0.986366|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.972922|1.000000|0.999997|0.999998|0.999997|0.000003|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.978219|0.986275|0.994464|0.972922|0.986368|0.986366|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|6142174|170949|0|53611496123|170949 / 53611667072|0 / 6142174|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|42828|124171|46778|6252|1.909188%|4|334089|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `IT_IT`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/norwegian-bokmal.md

@@ -26,7 +26,7 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
@@ -42,11 +42,11 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `norwegianBokmalRadixor` | 3.235 | 0.147 | 56.4 | 1.000 | Full Radixor dictionary patch-command stemmer. |
+| Radixor | `norwegianBokmalRadixor` | 3.631 | 1.377 | 63.3 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Lucene NorwegianMinimalStemFilter | `norwegianBokmalLuceneNorwegianMinimalStemFilter` | 2.720 | 0.190 | 47.4 | 0.841 | Minimal Norwegian suffix reducer. |
+| Lucene NorwegianMinimalStemFilter | `norwegianBokmalLuceneNorwegianMinimalStemFilter` | 2.910 | 0.177 | 50.7 | 0.801 | Minimal Norwegian suffix reducer. |
-| Lucene NorwegianLightStemFilter | `norwegianBokmalLuceneNorwegianLightStemFilter` | 3.189 | 0.261 | 55.6 | 0.986 | Light Norwegian suffix stemmer. |
+| Lucene NorwegianLightStemFilter | `norwegianBokmalLuceneNorwegianLightStemFilter` | 3.335 | 0.116 | 58.1 | 0.919 | Light Norwegian suffix stemmer. |
-| Official Snowball direct | `snowballDirect[NORWEGIAN_BOKMAL]` | 3.978 | 0.031 | 69.3 | 1.230 | Official Snowball generated Java stemmer; direct API. |
+| Official Snowball direct | `snowballDirect[NORWEGIAN_BOKMAL]` | 4.277 | 0.082 | 74.5 | 1.178 | Official Snowball generated Java stemmer; direct API. |
-| Lucene SnowballFilter | `luceneSnowballFilter[NORWEGIAN_BOKMAL]` | 5.526 | 0.409 | 96.3 | 1.708 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Lucene SnowballFilter | `luceneSnowballFilter[NORWEGIAN_BOKMAL]` | 6.077 | 0.208 | 105.9 | 1.674 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
 
 ## Interpretation Notes
 
@@ -55,3 +55,366 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `NB_NO` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/nb_no/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.974783** among 5 deterministic stemmers. The runner-up is `SNOWBALL NORWEGIAN BOKMAL DIRECT` at 0.874964, a difference of 0.099819. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.975000** among 5 deterministic stemmers. The runner-up is `SNOWBALL NORWEGIAN BOKMAL DIRECT` at 0.874991, a difference of 0.100009. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **7 result rows**, **5 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.974783|11482 / 2835618215 (0.000405%)|7170 / 142180 (5.042903%)|0.927078|0.935387|0.935488|
+|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.874964|23997 / 2835618215 (0.000846%)|35554 / 142180 (25.006330%)|0.802095|0.781707|0.782399|
+|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.874834|24046 / 2835618215 (0.000848%)|35591 / 142180 (25.032353%)|0.801759|0.781401|0.782091|
+|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.850006|25171 / 2835618215 (0.000888%)|42651 / 142180 (29.997890%)|0.776381|0.745871|0.747464|
+|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.832414|14772 / 2835618215 (0.000521%)|47654 / 142180 (33.516669%)|0.815763|0.751764|0.758263|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.921620|0.949571|0.999996|0.974783|0.999993|0.000007|
+|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.816288|0.749937|0.999992|0.874964|0.999979|0.000021|
+|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.815930|0.749676|0.999992|0.874834|0.999979|0.000021|
+|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.798148|0.700021|0.999991|0.850006|0.999976|0.000024|
+|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.864847|0.664833|0.999995|0.832414|0.999978|0.000022|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.927078|0.935387|0.943846|0.878617|0.935491|0.935488|
+|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.802095|0.781707|0.762330|0.641641|0.782409|0.782399|
+|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.801759|0.781401|0.762052|0.641229|0.782102|0.782091|
+|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.776381|0.745871|0.717668|0.594732|0.747476|0.747464|
+|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.815763|0.751764|0.697076|0.602261|0.758274|0.758263|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.935384|0.993354|0.994615|0.993984|0.993984|
+|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.781696|0.988328|0.971120|0.979648|0.979648|
+|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.781391|0.988295|0.971086|0.979615|0.979615|
+|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.745859|0.987774|0.965622|0.976573|0.976573|
+|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.751753|0.992089|0.962516|0.977079|0.977079|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|135010|11482|7170|2835606733|11482 / 2835618215|7170 / 142180|
+|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|106626|23997|35554|2835594218|23997 / 2835618215|35554 / 142180|
+|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|106589|24046|35591|2835594169|24046 / 2835618215|35591 / 142180|
+|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|99529|25171|42651|2835593044|25171 / 2835618215|42651 / 142180|
+|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|94526|14772|47654|2835603443|14772 / 2835618215|47654 / 142180|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 2835618215 (0.000000%)|0 / 142180 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|142180|0|0|2835618215|0 / 2835618215|0 / 142180|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999996|20161 / 2835618215 (0.000711%)|0 / 142180 (0.000000%)|0.898118|0.933794|0.935844|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.875811|1.000000|0.999993|0.999996|0.999993|0.000007|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.898118|0.933794|0.972422|0.875811|0.935848|0.935844|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|142180|20161|0|2835598054|20161 / 2835618215|0 / 142180|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|7170|11482|8679|4237|5.626079%|9|79825|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **7 result rows**, **5 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.975000|11482 / 2831176784 (0.000406%)|7104 / 142091 (4.999613%)|0.927151|0.935591|0.935695|
+|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.874991|23997 / 2831176784 (0.000848%)|35524 / 142091 (25.000880%)|0.802043|0.781698|0.782388|
+|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.874798|23993 / 2831176784 (0.000847%)|35579 / 142091 (25.039587%)|0.801914|0.781464|0.782161|
+|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.849947|25118 / 2831176784 (0.000887%)|42641 / 142091 (30.009642%)|0.776513|0.745896|0.747500|
+|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.832344|14719 / 2831176784 (0.000520%)|47644 / 142091 (33.530625%)|0.815950|0.751796|0.758325|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.921608|0.950004|0.999996|0.975000|0.999993|0.000007|
+|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.816205|0.749991|0.999992|0.874991|0.999979|0.000021|
+|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.816153|0.749604|0.999992|0.874798|0.999979|0.000021|
+|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.798359|0.699904|0.999991|0.849947|0.999976|0.000024|
+|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.865169|0.664694|0.999995|0.832344|0.999978|0.000022|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.927151|0.935591|0.944186|0.878976|0.935698|0.935695|
+|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.802043|0.781698|0.762360|0.641630|0.782398|0.782388|
+|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.801914|0.781464|0.762031|0.641314|0.782171|0.782161|
+|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.776513|0.745896|0.717603|0.594765|0.747512|0.747500|
+|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.815950|0.751796|0.696995|0.602302|0.758335|0.758325|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.935587|0.993348|0.994694|0.994020|0.994020|
+|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.781688|0.988318|0.971127|0.979647|0.979647|
+|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.781454|0.988310|0.971074|0.979616|0.979616|
+|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.745885|0.987789|0.965603|0.976570|0.976570|
+|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.751785|0.992107|0.962494|0.977076|0.977076|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|134987|11482|7104|2831165302|11482 / 2831176784|7104 / 142091|
+|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|106567|23997|35524|2831152787|23997 / 2831176784|35524 / 142091|
+|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|106512|23993|35579|2831152791|23993 / 2831176784|35579 / 142091|
+|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|99450|25118|42641|2831151666|25118 / 2831176784|42641 / 142091|
+|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|94447|14719|47644|2831162065|14719 / 2831176784|47644 / 142091|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 2831176784 (0.000000%)|0 / 142091 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|142091|0|0|2831176784|0 / 2831176784|0 / 142091|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999996|20161 / 2831176784 (0.000712%)|0 / 142091 (0.000000%)|0.898061|0.933756|0.935808|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.875743|1.000000|0.999993|0.999996|0.999993|0.000007|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.898061|0.933756|0.972405|0.875743|0.935811|0.935808|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|142091|20161|0|2831156623|20161 / 2831176784|0 / 142091|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|7104|11482|8679|4204|5.586637%|9|79733|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `NB_NO`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/norwegian-nynorsk.md

@@ -26,11 +26,11 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
-| Radixor | 93.089% | 91.395% | 96.863% | Radixor baseline in the Snowball-language comparison family. |
+| Radixor | 93.089% | 91.395% | 96.863% | Full Radixor dictionary patch-command stemmer. |
 | Official Snowball direct | 60.974% | 60.212% | 62.670% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
 | Lucene SnowballFilter | 60.918% | 60.146% | 62.638% | Lucene TokenFilter integration path around the Snowball algorithm. |
 
@@ -40,9 +40,9 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `radixor[NORWEGIAN_NYNORSK]` | 0.541 | 0.021 | 39.9 | 1.000 | Radixor baseline for the Snowball-language comparison family. |
+| Radixor | `radixor[NORWEGIAN_NYNORSK]` | 0.571 | 0.012 | 42.1 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Official Snowball direct | `snowballDirect[NORWEGIAN_NYNORSK]` | 0.855 | 0.008 | 63.0 | 1.580 | Official Snowball generated Java stemmer; direct API. |
+| Official Snowball direct | `snowballDirect[NORWEGIAN_NYNORSK]` | 0.919 | 0.038 | 67.7 | 1.609 | Official Snowball generated Java stemmer; direct API. |
-| Lucene SnowballFilter | `luceneSnowballFilter[NORWEGIAN_NYNORSK]` | 1.221 | 0.025 | 90.0 | 2.258 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Lucene SnowballFilter | `luceneSnowballFilter[NORWEGIAN_NYNORSK]` | 1.309 | 0.015 | 96.5 | 2.292 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
 
 ## Interpretation Notes
 
@@ -51,3 +51,346 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `NN_NO` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/nn_no/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.935777** among 3 deterministic stemmers. The runner-up is `SNOWBALL NORWEGIAN NYNORSK DIRECT` at 0.858908, a difference of 0.076869. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.935853** among 3 deterministic stemmers. The runner-up is `SNOWBALL NORWEGIAN NYNORSK DIRECT` at 0.859037, a difference of 0.076816. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **5 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.935777|6230 / 166491473 (0.003742%)|3936 / 30652 (12.840924%)|0.822355|0.840152|0.840669|
+|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.858908|8274 / 166491473 (0.004970%)|8648 / 30652 (28.213493%)|0.724941|0.722271|0.722234|
+|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.858484|8295 / 166491473 (0.004982%)|8674 / 30652 (28.298317%)|0.724180|0.721477|0.721440|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.810903|0.871591|0.999963|0.935777|0.999939|0.000061|
+|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.726732|0.717865|0.999950|0.858908|0.999898|0.000102|
+|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.725993|0.717017|0.999950|0.858484|0.999898|0.000102|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.822355|0.840152|0.858737|0.724364|0.840699|0.840669|
+|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.724941|0.722271|0.719621|0.565278|0.722285|0.722234|
+|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.724180|0.721477|0.718794|0.564305|0.721491|0.721440|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.840122|0.983845|0.986802|0.985321|0.985321|
+|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.722221|0.980542|0.964998|0.972708|0.972708|
+|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.721426|0.980461|0.964862|0.972599|0.972599|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|26716|6230|3936|166485243|6230 / 166491473|3936 / 30652|
+|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|22004|8274|8648|166483199|8274 / 166491473|8648 / 30652|
+|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|21978|8295|8674|166483178|8295 / 166491473|8674 / 30652|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 166491473 (0.000000%)|0 / 30652 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|30652|0|0|166491473|0 / 166491473|0 / 30652|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999960|13214 / 166491473 (0.007937%)|0 / 30652 (0.000000%)|0.743562|0.822674|0.835888|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.698764|1.000000|0.999921|0.999960|0.999921|0.000079|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.743562|0.822674|0.920624|0.698764|0.835921|0.835888|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|30652|13214|0|166478259|13214 / 166491473|0 / 30652|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|3936|6230|6984|2404|13.172603%|5|21513|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **5 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.935853|6230 / 165926276 (0.003755%)|3924 / 30595 (12.825625%)|0.822169|0.840084|0.840609|
+|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.859037|8274 / 165926276 (0.004987%)|8624 / 30595 (28.187612%)|0.724757|0.722255|0.722216|
+|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.858661|8274 / 165926276 (0.004987%)|8647 / 30595 (28.262788%)|0.724438|0.721772|0.721734|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.810644|0.871744|0.999962|0.935853|0.999939|0.000061|
+|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.726434|0.718124|0.999950|0.859037|0.999898|0.000102|
+|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.726226|0.717372|0.999950|0.858661|0.999898|0.000102|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.822169|0.840084|0.858798|0.724263|0.840639|0.840609|
+|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.724757|0.722255|0.719771|0.565258|0.722267|0.722216|
+|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.724438|0.721772|0.719126|0.564666|0.721785|0.721734|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.840054|0.983815|0.986842|0.985326|0.985326|
+|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.722204|0.980506|0.965065|0.972724|0.972724|
+|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.721721|0.980506|0.964945|0.972663|0.972663|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|26671|6230|3924|165920046|6230 / 165926276|3924 / 30595|
+|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|21971|8274|8624|165918002|8274 / 165926276|8624 / 30595|
+|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|21948|8274|8647|165918002|8274 / 165926276|8647 / 30595|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 165926276 (0.000000%)|0 / 30595 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|30595|0|0|165926276|0 / 165926276|0 / 30595|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999960|13214 / 165926276 (0.007964%)|0 / 30595 (0.000000%)|0.743207|0.822402|0.835654|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.698372|1.000000|0.999920|0.999960|0.999920|0.000080|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.743207|0.822402|0.920488|0.698372|0.835687|0.835654|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|30595|13214|0|165913062|13214 / 165926276|0 / 30595|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|3924|6230|6984|2399|13.167572%|5|21477|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `NN_NO`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/persian.md

@@ -24,7 +24,7 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
@@ -37,8 +37,8 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `persianRadixor` | 0.231 | 0.021 | 63.6 | 1.000 | Full Radixor dictionary patch-command stemmer. |
+| Radixor | `persianRadixor` | 0.245 | 0.025 | 49.0 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Lucene PersianStemFilter | `persianLucenePersianStemFilter` | 0.443 | 0.012 | 122.0 | 1.918 | Persian suffix stemmer with Lucene normalization in the measured path. |
+| Lucene PersianStemFilter | `persianLucenePersianStemFilter` | 0.466 | 0.015 | 93.3 | 1.902 | Persian suffix stemmer with Lucene normalization in the measured path. |
 
 ## Interpretation Notes
 
@@ -47,3 +47,336 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `FA_IR` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/fa_ir/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.974922** among 2 deterministic stemmers. The runner-up is `PERSIAN LUCENE PERSIAN STEM FILTER` at 0.502171, a difference of 0.472751. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.974922** among 2 deterministic stemmers. The runner-up is `PERSIAN LUCENE PERSIAN STEM FILTER` at 0.502171, a difference of 0.472751. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **4 result rows**, **2 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.974922|8621 / 6748402 (0.127749%)|4812 / 98448 (4.887860%)|0.922566|0.933071|0.932249|
+|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.502171|179 / 6748402 (0.002652%)|98018 / 98448 (99.563221%)|0.021312|0.008682|0.054801|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.915693|0.951121|0.998723|0.974922|0.998038|0.001962|
+|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.706076|0.004368|0.999973|0.502171|0.985658|0.014342|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.922566|0.933071|0.943818|0.874539|0.933239|0.932249|
+|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.021312|0.008682|0.005451|0.004360|0.055534|0.054801|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.932076|0.980343|0.984586|0.982460|0.982460|
+|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.008507|0.985686|0.520347|0.681125|0.681125|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|93636|8621|4812|6739781|8621 / 6748402|4812 / 98448|
+|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|430|179|98018|6748223|179 / 6748402|98018 / 98448|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 6748402 (0.000000%)|0 / 98448 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|98448|0|0|6748402|0 / 6748402|0 / 98448|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999005|13433 / 6748402 (0.199055%)|0 / 98448 (0.000000%)|0.901585|0.936133|0.937114|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.879935|1.000000|0.998009|0.999005|0.998038|0.001962|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.901585|0.936133|0.973435|0.879935|0.938048|0.937114|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|98448|13433|0|6734969|13433 / 6748402|0 / 98448|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|4812|8621|4812|314|8.484193%|2|4015|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **4 result rows**, **2 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.974922|8621 / 6748402 (0.127749%)|4812 / 98448 (4.887860%)|0.922566|0.933071|0.932249|
+|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.502171|179 / 6748402 (0.002652%)|98018 / 98448 (99.563221%)|0.021312|0.008682|0.054801|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.915693|0.951121|0.998723|0.974922|0.998038|0.001962|
+|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.706076|0.004368|0.999973|0.502171|0.985658|0.014342|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.922566|0.933071|0.943818|0.874539|0.933239|0.932249|
+|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.021312|0.008682|0.005451|0.004360|0.055534|0.054801|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.932076|0.980343|0.984586|0.982460|0.982460|
+|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.008507|0.985686|0.520347|0.681125|0.681125|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|93636|8621|4812|6739781|8621 / 6748402|4812 / 98448|
+|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|430|179|98018|6748223|179 / 6748402|98018 / 98448|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 6748402 (0.000000%)|0 / 98448 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|98448|0|0|6748402|0 / 6748402|0 / 98448|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999005|13433 / 6748402 (0.199055%)|0 / 98448 (0.000000%)|0.901585|0.936133|0.937114|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.879935|1.000000|0.998009|0.999005|0.998038|0.001962|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.901585|0.936133|0.973435|0.879935|0.938048|0.937114|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|98448|13433|0|6734969|13433 / 6748402|0 / 98448|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|4812|8621|4812|314|8.484193%|2|4015|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `FA_IR`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/polish.md

@@ -26,11 +26,12 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
 | Radixor | 98.837% | 98.744% | 99.359% | Full Radixor dictionary patch-command stemmer. |
+| Lucene HunspellStemFilter | 89.545% | 88.272% | 96.713% | Benchmark-only Polish Hunspell dictionary compared via Lucene HunspellStemFilter. |
 | Lucene MorfologikFilter | 87.729% | 86.606% | 94.047% | Dictionary-based path; Morfologik can emit multiple terms. |
 | Lucene StempelFilter | 70.009% | 69.262% | 74.220% | Lucene TokenFilter integration path for table-driven Polish Stempel. |
 | Lucene StempelStemmer direct | 70.009% | 69.262% | 74.220% | Direct table-driven Polish Stempel stemmer API. |
@@ -41,10 +42,11 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `polishRadixor` | 7.760 | 0.240 | 69.1 | 1.000 | Full Radixor dictionary patch-command stemmer. |
+| Radixor | `polishRadixor` | 9.049 | 0.485 | 80.5 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Lucene StempelStemmer direct | `polishLuceneStempelStemmerDirect` | 34.295 | 0.418 | 305.3 | 4.420 | Direct table-driven Polish Stempel stemmer API. |
+| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 483.316 | 11.455 | 4301.8 | 53.408 | Benchmark-only Polish Hunspell dictionary compared via Lucene HunspellStemFilter. |
-| Lucene StempelFilter | `polishLuceneStempelFilter` | 39.116 | 1.717 | 348.2 | 5.041 | Lucene TokenFilter integration path for table-driven Polish Stempel. |
+| Lucene StempelStemmer direct | `polishLuceneStempelStemmerDirect` | 41.932 | 1.916 | 373.2 | 4.634 | Direct table-driven Polish Stempel stemmer API. |
-| Lucene MorfologikFilter | `polishLuceneMorfologikFilter` | 128.516 | 12.557 | 1143.9 | 16.562 | Dictionary-based Morfologik TokenFilter; may emit multiple terms. |
+| Lucene StempelFilter | `polishLuceneStempelFilter` | 45.277 | 13.693 | 403.0 | 5.003 | Lucene TokenFilter integration path for table-driven Polish Stempel. |
+| Lucene MorfologikFilter | `polishLuceneMorfologikFilter` | 135.763 | 31.634 | 1208.4 | 15.002 | Dictionary-based Morfologik TokenFilter; may emit multiple terms. |
 
 ## Interpretation Notes
 
@@ -53,3 +55,410 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `PL_PL` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/pl_pl/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.990388** among 5 deterministic stemmers. The runner-up is `POLISH LUCENE MORFOLOGIK FILTER` at 0.948154, a difference of 0.042234. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.990579** among 5 deterministic stemmers. The runner-up is `POLISH LUCENE MORFOLOGIK FILTER` at 0.948177, a difference of 0.042402. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **11 result rows**, **5 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.990388|13669 / 7482478003 (0.000183%)|21547 / 1120967 (1.922180%)|0.986324|0.984237|0.984241|
+|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.948154|99228 / 7482478003 (0.001326%)|116220 / 1120967 (10.367834%)|0.907324|0.903167|0.903179|
+|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.933222|52652 / 7482478003 (0.000704%)|149705 / 1120967 (13.354987%)|0.930930|0.905656|0.906571|
+|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.855748|66669 / 7482478003 (0.000891%)|323394 / 1120967 (28.849556%)|0.871106|0.803515|0.810296|
+|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.855748|66669 / 7482478003 (0.000891%)|323394 / 1120967 (28.849556%)|0.871106|0.803515|0.810296|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.987720|0.980778|0.999998|0.990388|0.999995|0.000005|
+|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.910118|0.896322|0.999987|0.948154|0.999971|0.000029|
+|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.948578|0.866450|0.999993|0.933222|0.999973|0.000027|
+|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.922858|0.711504|0.999991|0.855748|0.999948|0.000052|
+|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.922858|0.711504|0.999991|0.855748|0.999948|0.000052|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.986324|0.984237|0.982159|0.968963|0.984243|0.984241|
+|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.907324|0.903167|0.899047|0.823432|0.903193|0.903179|
+|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.930930|0.905656|0.881718|0.827579|0.906584|0.906571|
+|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.871106|0.803515|0.745659|0.671564|0.810320|0.810296|
+|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.871106|0.803515|0.745659|0.671564|0.810320|0.810296|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.984234|0.996967|0.996469|0.996718|0.996718|
+|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.903153|0.990022|0.977054|0.983495|0.983495|
+|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.905642|0.994546|0.970520|0.982386|0.982386|
+|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.803490|0.991767|0.931069|0.960460|0.960460|
+|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.803490|0.991767|0.931069|0.960460|0.960460|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|1099420|13669|21547|7482464334|13669 / 7482478003|21547 / 1120967|
+|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|1004747|99228|116220|7482378775|99228 / 7482478003|116220 / 1120967|
+|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|971262|52652|149705|7482425351|52652 / 7482478003|149705 / 1120967|
+|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|797573|66669|323394|7482411334|66669 / 7482478003|323394 / 1120967|
+|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|797573|66669|323394|7482411334|66669 / 7482478003|323394 / 1120967|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 7482478003 (0.000000%)|0 / 1120967 (0.000000%)|1.000000|1.000000|1.000000|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.987570|85532 / 7482478003 (0.001143%)|27855 / 1120967 (2.484908%)|0.936598|0.950693|0.950985|
+|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|0.963982|42213 / 7482478003 (0.000564%)|80743 / 1120967 (7.202977%)|0.954209|0.944197|0.944333|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.927432|0.975151|0.999989|0.987570|0.999985|0.000015|
+|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|0.961002|0.927970|0.999994|0.963982|0.999984|0.000016|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.936598|0.950693|0.965218|0.906020|0.950992|0.950985|
+|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|0.954209|0.944197|0.934394|0.894293|0.944342|0.944333|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1120967|0|0|7482478003|0 / 7482478003|0 / 1120967|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|1093112|85532|27855|7482392471|85532 / 7482478003|27855 / 1120967|
+|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|1040224|42213|80743|7482435790|42213 / 7482478003|80743 / 1120967|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999997|38073 / 7482478003 (0.000509%)|0 / 1120967 (0.000000%)|0.973547|0.983301|0.983436|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.987566|143096 / 7482478003 (0.001912%)|27855 / 1120967 (2.484908%)|0.901045|0.927476|0.928576|
+|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|0.963980|82745 / 7482478003 (0.001106%)|80743 / 1120967 (7.202977%)|0.926646|0.927142|0.927132|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.967151|1.000000|0.999995|0.999997|0.999995|0.000005|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.884246|0.975151|0.999981|0.987566|0.999977|0.000023|
+|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|0.926316|0.927970|0.999989|0.963980|0.999978|0.000022|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.973547|0.983301|0.993253|0.967151|0.983438|0.983436|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.901045|0.927476|0.955505|0.864761|0.928587|0.928576|
+|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|0.926646|0.927142|0.927639|0.864180|0.927143|0.927132|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|1120967|38073|0|7482439930|38073 / 7482478003|0 / 1120967|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|1093112|143096|27855|7482334907|143096 / 7482478003|27855 / 1120967|
+|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|1040224|82745|80743|7482395258|82745 / 7482478003|80743 / 1120967|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|HUNSPELL POLISH LUCENE FILTER|68962|10439|30093|11447|9.356634%|6|135231|
+|POLISH LUCENE MORFOLOGIK FILTER|88365|13696|43868|12873|10.522229%|5|136636|
+|Radixor|21547|13669|24404|2866|2.342632%|4|125778|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **11 result rows**, **5 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.990579|13669 / 7310252699 (0.000187%)|21000 / 1114651 (1.883998%)|0.986350|0.984397|0.984400|
+|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.948177|99224 / 7310252699 (0.001357%)|115513 / 1114651 (10.363154%)|0.906972|0.902966|0.902976|
+|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.933309|51950 / 7310252699 (0.000711%)|148667 / 1114651 (13.337538%)|0.931269|0.905928|0.906847|
+|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.856382|66274 / 7310252699 (0.000907%)|320158 / 1114651 (28.722712%)|0.871591|0.804380|0.811082|
+|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.856382|66274 / 7310252699 (0.000907%)|320158 / 1114651 (28.722712%)|0.871591|0.804380|0.811082|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.987656|0.981160|0.999998|0.990579|0.999995|0.000005|
+|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.909662|0.896368|0.999986|0.948177|0.999971|0.000029|
+|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.948965|0.866625|0.999993|0.933309|0.999973|0.000027|
+|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.923006|0.712773|0.999991|0.856382|0.999947|0.000053|
+|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.923006|0.712773|0.999991|0.856382|0.999947|0.000053|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.986350|0.984397|0.982452|0.969274|0.984403|0.984400|
+|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.906972|0.902966|0.898996|0.823098|0.902991|0.902976|
+|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.931269|0.905928|0.881929|0.828033|0.906861|0.906847|
+|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.871591|0.804380|0.746792|0.672772|0.811106|0.811082|
+|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.871591|0.804380|0.746792|0.672772|0.811106|0.811082|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.984395|0.996926|0.996647|0.996786|0.996786|
+|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.902952|0.989889|0.977012|0.983408|0.983408|
+|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.905914|0.994584|0.970514|0.982402|0.982402|
+|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.804354|0.991711|0.931318|0.960566|0.960566|
+|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.804354|0.991711|0.931318|0.960566|0.960566|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|1093651|13669|21000|7310239030|13669 / 7310252699|21000 / 1114651|
+|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|999138|99224|115513|7310153475|99224 / 7310252699|115513 / 1114651|
+|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|965984|51950|148667|7310200749|51950 / 7310252699|148667 / 1114651|
+|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|794493|66274|320158|7310186425|66274 / 7310252699|320158 / 1114651|
+|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|794493|66274|320158|7310186425|66274 / 7310252699|320158 / 1114651|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 7310252699 (0.000000%)|0 / 1114651 (0.000000%)|1.000000|1.000000|1.000000|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.987661|85532 / 7310252699 (0.001170%)|27494 / 1114651 (2.466602%)|0.936331|0.950586|0.950885|
+|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|0.963946|41671 / 7310252699 (0.000570%)|80368 / 1114651 (7.210149%)|0.954406|0.944290|0.944429|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.927063|0.975334|0.999988|0.987661|0.999985|0.000015|
+|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|0.961271|0.927899|0.999994|0.963946|0.999983|0.000017|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.936331|0.950586|0.965282|0.905826|0.950892|0.950885|
+|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|0.954406|0.944290|0.934386|0.894459|0.944437|0.944429|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1114651|0|0|7310252699|0 / 7310252699|0 / 1114651|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|1087157|85532|27494|7310167167|85532 / 7310252699|27494 / 1114651|
+|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|1034283|41671|80368|7310211028|41671 / 7310252699|80368 / 1114651|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999997|38073 / 7310252699 (0.000521%)|0 / 1114651 (0.000000%)|0.973401|0.983208|0.983344|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.987657|143085 / 7310252699 (0.001957%)|27494 / 1114651 (2.466602%)|0.900618|0.927255|0.928372|
+|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|0.963944|81865 / 7310252699 (0.001120%)|80368 / 1114651 (7.210149%)|0.926903|0.927276|0.927265|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.966971|1.000000|0.999995|0.999997|0.999995|0.000005|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.883694|0.975334|0.999980|0.987657|0.999977|0.000023|
+|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|0.926654|0.927899|0.999989|0.963944|0.999978|0.000022|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.973401|0.983208|0.993215|0.966971|0.983347|0.983344|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.900618|0.927255|0.955516|0.864376|0.928384|0.928372|
+|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|0.926903|0.927276|0.927649|0.864412|0.927276|0.927265|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|1114651|38073|0|7310214626|38073 / 7310252699|0 / 1114651|
+|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|1087157|143085|27494|7310109614|143085 / 7310252699|27494 / 1114651|
+|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|1034283|81865|80368|7310170834|81865 / 7310252699|80368 / 1114651|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|HUNSPELL POLISH LUCENE FILTER|68299|10279|29915|11265|9.315692%|6|133595|
+|POLISH LUCENE MORFOLOGIK FILTER|88019|13692|43861|12763|10.554476%|5|135105|
+|Radixor|21000|13669|24404|2780|2.298946%|4|124274|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `PL_PL`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/portuguese.md

@@ -26,7 +26,7 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
@@ -43,12 +43,12 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `portugueseRadixor` | 10.598 | 0.273 | 51.1 | 1.000 | Full Radixor dictionary patch-command stemmer. |
+| Radixor | `portugueseRadixor` | 12.109 | 0.698 | 58.4 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Lucene PortugueseLightStemFilter | `portugueseLucenePortugueseLightStemFilter` | 10.101 | 0.389 | 48.7 | 0.953 | Light Portuguese suffix stemmer. |
+| Lucene PortugueseLightStemFilter | `portugueseLucenePortugueseLightStemFilter` | 11.172 | 1.870 | 53.8 | 0.923 | Light Portuguese suffix stemmer. |
-| Lucene PortugueseMinimalStemFilter | `portugueseLucenePortugueseMinimalStemFilter` | 14.493 | 0.760 | 69.8 | 1.367 | Minimal Portuguese suffix reducer. |
+| Lucene PortugueseMinimalStemFilter | `portugueseLucenePortugueseMinimalStemFilter` | 16.038 | 1.752 | 77.3 | 1.325 | Minimal Portuguese suffix reducer. |
-| Official Snowball direct | `snowballDirect[PORTUGUESE]` | 52.508 | 6.338 | 253.1 | 4.954 | Official Snowball generated Java stemmer; direct API. |
+| Official Snowball direct | `snowballDirect[PORTUGUESE]` | 53.725 | 5.356 | 258.9 | 4.437 | Official Snowball generated Java stemmer; direct API. |
-| Lucene SnowballFilter | `luceneSnowballFilter[PORTUGUESE]` | 54.048 | 1.173 | 260.5 | 5.100 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Lucene SnowballFilter | `luceneSnowballFilter[PORTUGUESE]` | 57.457 | 1.182 | 276.9 | 4.745 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
-| Lucene PortugueseStemFilter | `portugueseLucenePortugueseStemFilter` | 141.208 | 12.785 | 680.6 | 13.324 | Portuguese RSLP-style Lucene TokenFilter. |
+| Lucene PortugueseStemFilter | `portugueseLucenePortugueseStemFilter` | 165.447 | 40.334 | 797.4 | 13.663 | Portuguese RSLP-style Lucene TokenFilter. |
 
 ## Interpretation Notes
 
@@ -57,3 +57,376 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `PT_PT` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/pt_pt/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.998502** among 6 deterministic stemmers. The runner-up is `SNOWBALL PORTUGUESE DIRECT` at 0.938800, a difference of 0.059702. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.998502** among 6 deterministic stemmers. The runner-up is `SNOWBALL PORTUGUESE DIRECT` at 0.938800, a difference of 0.059702. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **8 result rows**, **6 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.998502|20678 / 22358203756 (0.000092%)|16444 / 5489060 (0.299578%)|0.996389|0.996620|0.996619|
+|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.938800|167230 / 22358203756 (0.000748%)|671821 / 5489060 (12.239272%)|0.947271|0.919888|0.920940|
+|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.938800|167230 / 22358203756 (0.000748%)|671821 / 5489060 (12.239272%)|0.947271|0.919888|0.920940|
+|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.846459|99075 / 22358203756 (0.000443%)|1685572 / 5489060 (30.707844%)|0.901330|0.809975|0.821750|
+|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.513648|2511 / 22358203756 (0.000011%)|5339230 / 5489060 (97.270389%)|0.122843|0.053118|0.163828|
+|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.503954|598 / 22358203756 (0.000003%)|5445654 / 5489060 (99.209227%)|0.038310|0.015690|0.088308|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.996236|0.997004|0.999999|0.998502|0.999998|0.000002|
+|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.966450|0.877607|0.999993|0.938800|0.999962|0.000038|
+|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.966450|0.877607|0.999993|0.938800|0.999962|0.000038|
+|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.974613|0.692922|0.999996|0.846459|0.999920|0.000080|
+|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.983517|0.027296|1.000000|0.513648|0.999761|0.000239|
+|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.986410|0.007908|1.000000|0.503954|0.999756|0.000244|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.996389|0.996620|0.996850|0.993262|0.996620|0.996619|
+|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.947271|0.919888|0.894045|0.851661|0.920958|0.920940|
+|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.947271|0.919888|0.894045|0.851661|0.920958|0.920940|
+|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.901330|0.809975|0.735434|0.680636|0.821785|0.821750|
+|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.122843|0.053118|0.033885|0.027284|0.163848|0.163828|
+|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.038310|0.015690|0.009865|0.007907|0.088319|0.088308|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.996619|0.999299|0.999347|0.999323|0.999323|
+|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.919870|0.996663|0.967924|0.982083|0.982083|
+|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.919870|0.996663|0.967924|0.982083|0.982083|
+|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.809936|0.996729|0.918475|0.956003|0.956003|
+|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.053105|0.999226|0.720580|0.837330|0.837330|
+|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.015686|0.999664|0.692383|0.818122|0.818122|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|5472616|20678|16444|22358183078|20678 / 22358203756|16444 / 5489060|
+|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|4817239|167230|671821|22358036526|167230 / 22358203756|671821 / 5489060|
+|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|4817239|167230|671821|22358036526|167230 / 22358203756|671821 / 5489060|
+|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|3803488|99075|1685572|22358104681|99075 / 22358203756|1685572 / 5489060|
+|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|149830|2511|5339230|22358201245|2511 / 22358203756|5339230 / 5489060|
+|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|43406|598|5445654|22358203158|598 / 22358203756|5445654 / 5489060|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 22358203756 (0.000000%)|0 / 5489060 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|5489060|0|0|22358203756|0 / 22358203756|0 / 5489060|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999999|38310 / 22358203756 (0.000171%)|0 / 5489060 (0.000000%)|0.994448|0.996522|0.996528|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.993069|1.000000|0.999998|0.999999|0.999998|0.000002|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.994448|0.996522|0.998606|0.993069|0.996528|0.996528|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|5489060|38310|0|22358165446|38310 / 22358203756|0 / 5489060|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|16444|20678|17632|790|0.373542%|3|212297|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **8 result rows**, **6 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.998502|20678 / 22358203756 (0.000092%)|16444 / 5489060 (0.299578%)|0.996389|0.996620|0.996619|
+|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.938800|167230 / 22358203756 (0.000748%)|671821 / 5489060 (12.239272%)|0.947271|0.919888|0.920940|
+|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.938800|167230 / 22358203756 (0.000748%)|671821 / 5489060 (12.239272%)|0.947271|0.919888|0.920940|
+|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.846459|99075 / 22358203756 (0.000443%)|1685572 / 5489060 (30.707844%)|0.901330|0.809975|0.821750|
+|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.513648|2511 / 22358203756 (0.000011%)|5339230 / 5489060 (97.270389%)|0.122843|0.053118|0.163828|
+|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.503954|598 / 22358203756 (0.000003%)|5445654 / 5489060 (99.209227%)|0.038310|0.015690|0.088308|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.996236|0.997004|0.999999|0.998502|0.999998|0.000002|
+|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.966450|0.877607|0.999993|0.938800|0.999962|0.000038|
+|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.966450|0.877607|0.999993|0.938800|0.999962|0.000038|
+|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.974613|0.692922|0.999996|0.846459|0.999920|0.000080|
+|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.983517|0.027296|1.000000|0.513648|0.999761|0.000239|
+|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.986410|0.007908|1.000000|0.503954|0.999756|0.000244|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.996389|0.996620|0.996850|0.993262|0.996620|0.996619|
+|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.947271|0.919888|0.894045|0.851661|0.920958|0.920940|
+|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.947271|0.919888|0.894045|0.851661|0.920958|0.920940|
+|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.901330|0.809975|0.735434|0.680636|0.821785|0.821750|
+|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.122843|0.053118|0.033885|0.027284|0.163848|0.163828|
+|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.038310|0.015690|0.009865|0.007907|0.088319|0.088308|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.996619|0.999299|0.999347|0.999323|0.999323|
+|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.919870|0.996663|0.967924|0.982083|0.982083|
+|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.919870|0.996663|0.967924|0.982083|0.982083|
+|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.809936|0.996729|0.918475|0.956003|0.956003|
+|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.053105|0.999226|0.720580|0.837330|0.837330|
+|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.015686|0.999664|0.692383|0.818122|0.818122|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|5472616|20678|16444|22358183078|20678 / 22358203756|16444 / 5489060|
+|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|4817239|167230|671821|22358036526|167230 / 22358203756|671821 / 5489060|
+|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|4817239|167230|671821|22358036526|167230 / 22358203756|671821 / 5489060|
+|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|3803488|99075|1685572|22358104681|99075 / 22358203756|1685572 / 5489060|
+|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|149830|2511|5339230|22358201245|2511 / 22358203756|5339230 / 5489060|
+|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|43406|598|5445654|22358203158|598 / 22358203756|5445654 / 5489060|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 22358203756 (0.000000%)|0 / 5489060 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|5489060|0|0|22358203756|0 / 22358203756|0 / 5489060|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999999|38310 / 22358203756 (0.000171%)|0 / 5489060 (0.000000%)|0.994448|0.996522|0.996528|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.993069|1.000000|0.999998|0.999999|0.999998|0.000002|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.994448|0.996522|0.998606|0.993069|0.996528|0.996528|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|5489060|38310|0|22358165446|38310 / 22358203756|0 / 5489060|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|16444|20678|17632|790|0.373542%|3|212297|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `PT_PT`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/russian.md

@@ -26,7 +26,7 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
@@ -41,10 +41,10 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `russianRadixor` | 72.970 | 15.642 | 99.8 | 1.000 | Full Radixor dictionary patch-command stemmer. |
+| Radixor | `russianRadixor` | 89.671 | 3.886 | 122.6 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Lucene RussianLightStemFilter | `russianLuceneRussianLightStemFilter` | 57.900 | 4.404 | 79.2 | 0.793 | Light Russian suffix stemmer. |
+| Lucene RussianLightStemFilter | `russianLuceneRussianLightStemFilter` | 60.522 | 5.310 | 82.7 | 0.675 | Light Russian suffix stemmer. |
-| Official Snowball direct | `snowballDirect[RUSSIAN]` | 99.019 | 14.688 | 135.4 | 1.357 | Official Snowball generated Java stemmer; direct API. |
+| Official Snowball direct | `snowballDirect[RUSSIAN]` | 106.031 | 9.287 | 145.0 | 1.182 | Official Snowball generated Java stemmer; direct API. |
-| Lucene SnowballFilter | `luceneSnowballFilter[RUSSIAN]` | 128.272 | 7.201 | 175.4 | 1.758 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Lucene SnowballFilter | `luceneSnowballFilter[RUSSIAN]` | 137.512 | 10.801 | 188.0 | 1.534 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
 
 ## Interpretation Notes
 
@@ -53,3 +53,356 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `RU_RU` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/ru_ru/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.989827** among 4 deterministic stemmers. The runner-up is `SNOWBALL RUSSIAN LUCENE FILTER` at 0.834876, a difference of 0.154951. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.989852** among 4 deterministic stemmers. The runner-up is `SNOWBALL RUSSIAN DIRECT` at 0.834854, a difference of 0.154998. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.989827|155850 / 295576291016 (0.000053%)|266302 / 13089505 (2.034470%)|0.986313|0.983806|0.983814|
+|2|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.834876|3785790 / 295576291016 (0.001281%)|4322616 / 13089505 (33.023525%)|0.692485|0.683786|0.683923|
+|3|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.834867|3782908 / 295576291016 (0.001280%)|4322849 / 13089505 (33.025305%)|0.692603|0.683851|0.683989|
+|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.617692|321183 / 295576291016 (0.000109%)|10008438 / 13089505 (76.461547%)|0.577011|0.373649|0.461687|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.987992|0.979655|0.999999|0.989827|0.999999|0.000001|
+|2|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.698408|0.669765|0.999987|0.834876|0.999973|0.000027|
+|3|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.698563|0.669747|0.999987|0.834867|0.999973|0.000027|
+|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.905597|0.235385|0.999999|0.617692|0.999965|0.000035|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.986313|0.983806|0.981311|0.968128|0.983815|0.983814|
+|2|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.692485|0.683786|0.675304|0.519510|0.683936|0.683923|
+|3|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.692603|0.683851|0.675318|0.519585|0.684003|0.683989|
+|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.577011|0.373649|0.276278|0.229747|0.461696|0.461687|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.983805|0.997699|0.997274|0.997487|0.997487|
+|2|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.683773|0.974131|0.953674|0.963794|0.963794|
+|3|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.683838|0.974180|0.953661|0.963811|0.963811|
+|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.373638|0.994311|0.870888|0.928516|0.928516|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|12823203|155850|266302|295576135166|155850 / 295576291016|266302 / 13089505|
+|2|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|8766889|3785790|4322616|295572505226|3785790 / 295576291016|4322616 / 13089505|
+|3|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|8766656|3782908|4322849|295572508108|3782908 / 295576291016|4322849 / 13089505|
+|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|3081067|321183|10008438|295575969833|321183 / 295576291016|10008438 / 13089505|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 295576291016 (0.000000%)|13 / 13089505 (0.000099%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0.999999|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|0.999999|0.999999|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|13089492|0|13|295576291016|0 / 295576291016|13 / 13089505|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999999|434710 / 295576291016 (0.000147%)|13 / 13089505 (0.000099%)|0.974119|0.983665|0.983796|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.967857|0.999999|0.999999|0.999999|0.999999|0.000001|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.974119|0.983665|0.993401|0.967856|0.983797|0.983796|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|13089492|434710|13|295575856306|434710 / 295576291016|13 / 13089505|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|266289|155850|278860|19162|2.492190%|4|788492|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.989852|155850 / 295000681652 (0.000053%)|265613 / 13087126 (2.029575%)|0.986322|0.983830|0.983838|
+|2|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.834854|3782908 / 295000681652 (0.001282%)|4322407 / 13087126 (33.027931%)|0.692561|0.683815|0.683953|
+|3|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.834854|3782908 / 295000681652 (0.001282%)|4322407 / 13087126 (33.027931%)|0.692561|0.683815|0.683953|
+|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.617630|318921 / 295000681652 (0.000108%)|10008238 / 13087126 (76.473918%)|0.577038|0.373540|0.461703|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.987991|0.979704|0.999999|0.989852|0.999999|0.000001|
+|2|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.698516|0.669721|0.999987|0.834854|0.999973|0.000027|
+|3|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.698516|0.669721|0.999987|0.834854|0.999973|0.000027|
+|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.906139|0.235261|0.999999|0.617630|0.999965|0.000035|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.986322|0.983830|0.981350|0.968175|0.983839|0.983838|
+|2|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.692561|0.683815|0.675288|0.519544|0.683967|0.683953|
+|3|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.692561|0.683815|0.675288|0.519544|0.683967|0.683953|
+|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.577038|0.373540|0.276152|0.229664|0.461713|0.461703|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.983829|0.997697|0.997321|0.997509|0.997509|
+|2|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.683802|0.974149|0.953634|0.963782|0.963782|
+|3|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.683802|0.974149|0.953634|0.963782|0.963782|
+|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.373528|0.994350|0.870767|0.928464|0.928464|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|12821513|155850|265613|295000525802|155850 / 295000681652|265613 / 13087126|
+|2|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|8764719|3782908|4322407|294996898744|3782908 / 295000681652|4322407 / 13087126|
+|3|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|8764719|3782908|4322407|294996898744|3782908 / 295000681652|4322407 / 13087126|
+|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|3078888|318921|10008238|295000362731|318921 / 295000681652|10008238 / 13087126|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 295000681652 (0.000000%)|0 / 13087126 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|13087126|0|0|295000681652|0 / 295000681652|0 / 13087126|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999999|434710 / 295000681652 (0.000147%)|0 / 13087126 (0.000000%)|0.974115|0.983663|0.983794|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.967851|1.000000|0.999999|0.999999|0.999999|0.000001|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.974115|0.983663|0.993401|0.967851|0.983794|0.983794|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|13087126|434710|0|295000246942|434710 / 295000681652|0 / 13087126|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|265613|155850|278860|18991|2.472358%|4|787549|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `RU_RU`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/spanish.md

@@ -26,11 +26,12 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
 | Radixor | 97.459% | 97.544% | 96.891% | Full Radixor dictionary patch-command stemmer. |
+| Lucene HunspellStemFilter | 49.074% | 42.656% | 92.154% | Benchmark-only Spanish Hunspell dictionary compared via Lucene HunspellStemFilter. |
 | Lucene SpanishMinimalStemFilter | 17.284% | 5.347% | 97.403% | Minimal suffix reducer; narrow baseline, not a full stemmer. |
 | Lucene SpanishPluralStemFilter | 15.140% | 5.802% | 77.820% | Plural-focused suffix reducer; narrow baseline. |
 | Lucene SpanishLightStemFilter | 9.577% | 7.088% | 26.279% | Light suffix stemmer; intentionally narrower than a dictionary-derived stemmer. |
@@ -43,12 +44,13 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `spanishRadixor` | 64.539 | 3.448 | 80.0 | 1.000 | Full Radixor dictionary patch-command stemmer. |
+| Radixor | `spanishRadixor` | 78.919 | 7.253 | 97.9 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Lucene SpanishMinimalStemFilter | `spanishLuceneSpanishMinimalStemFilter` | 38.288 | 2.382 | 47.5 | 0.593 | Minimal Spanish suffix reducer; narrow baseline. |
+| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 2079.041 | 193.548 | 2578.6 | 26.344 | Benchmark-only Spanish Hunspell dictionary compared via Lucene HunspellStemFilter. |
-| Lucene SpanishLightStemFilter | `spanishLuceneSpanishLightStemFilter` | 40.855 | 2.407 | 50.7 | 0.633 | Light Spanish suffix stemmer. |
+| Lucene SpanishMinimalStemFilter | `spanishLuceneSpanishMinimalStemFilter` | 45.596 | 4.639 | 56.6 | 0.578 | Minimal Spanish suffix reducer; narrow baseline. |
-| Lucene SpanishPluralStemFilter | `spanishLuceneSpanishPluralStemFilter` | 91.054 | 8.822 | 112.9 | 1.411 | Plural-oriented Spanish suffix reducer. |
+| Lucene SpanishLightStemFilter | `spanishLuceneSpanishLightStemFilter` | 42.003 | 1.683 | 52.1 | 0.532 | Light Spanish suffix stemmer. |
-| Official Snowball direct | `snowballDirect[SPANISH]` | 168.813 | 32.860 | 209.4 | 2.616 | Official Snowball generated Java stemmer; direct API. |
+| Lucene SpanishPluralStemFilter | `spanishLuceneSpanishPluralStemFilter` | 93.734 | 6.247 | 116.3 | 1.188 | Plural-oriented Spanish suffix reducer. |
-| Lucene SnowballFilter | `luceneSnowballFilter[SPANISH]` | 185.626 | 50.297 | 230.2 | 2.876 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Official Snowball direct | `snowballDirect[SPANISH]` | 171.995 | 11.035 | 213.3 | 2.179 | Official Snowball generated Java stemmer; direct API. |
+| Lucene SnowballFilter | `luceneSnowballFilter[SPANISH]` | 211.138 | 17.940 | 261.9 | 2.675 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
 
 ## Interpretation Notes
 
@@ -57,3 +59,408 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `ES_ES` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/es_es/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.989295** among 7 deterministic stemmers. The runner-up is `SNOWBALL SPANISH LUCENE FILTER` at 0.652614, a difference of 0.336680. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.989429** among 7 deterministic stemmers. The runner-up is `SNOWBALL SPANISH DIRECT` at 0.652720, a difference of 0.336709. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **11 result rows**, **7 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.989295|288483 / 379567318110 (0.000076%)|898652 / 41973336 (2.141007%)|0.990105|0.985755|0.985780|
+|2|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.652614|2230481 / 379567318110 (0.000588%)|29161643 / 41973336 (69.476591%)|0.627151|0.449411|0.509848|
+|3|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.652614|2228819 / 379567318110 (0.000587%)|29161649 / 41973336 (69.476605%)|0.627192|0.449424|0.509876|
+|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.615102|536192 / 379567318110 (0.000141%)|32310860 / 41973336 (76.979490%)|0.583708|0.370408|0.466992|
+|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.514823|147956 / 379567318110 (0.000039%)|40729019 / 41973336 (97.035458%)|0.130864|0.057387|0.162762|
+|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.503874|58578 / 379567318110 (0.000015%)|41648091 / 41973336 (99.225115%)|0.037377|0.015357|0.081026|
+|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.501768|47859 / 379567318110 (0.000013%)|41824873 / 41973336 (99.646292%)|0.017361|0.007041|0.051714|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.993026|0.978590|0.999999|0.989295|0.999997|0.000003|
+|2|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.851718|0.305234|0.999994|0.652614|0.999917|0.000083|
+|3|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.851812|0.305234|0.999994|0.652614|0.999917|0.000083|
+|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.947425|0.230205|0.999999|0.615102|0.999913|0.000087|
+|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.893731|0.029645|1.000000|0.514823|0.999892|0.000108|
+|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.847383|0.007749|1.000000|0.503874|0.999890|0.000110|
+|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.756222|0.003537|1.000000|0.501768|0.999890|0.000110|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.990105|0.985755|0.981443|0.971910|0.985781|0.985780|
+|2|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.627151|0.449411|0.350170|0.289832|0.509876|0.509848|
+|3|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.627192|0.449424|0.350173|0.289843|0.509904|0.509876|
+|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.583708|0.370408|0.271278|0.227301|0.467014|0.466992|
+|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.130864|0.057387|0.036752|0.029541|0.162773|0.162762|
+|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.037377|0.015357|0.009664|0.007738|0.081032|0.081026|
+|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.017361|0.007041|0.004416|0.003533|0.051719|0.051714|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.985753|0.995418|0.993266|0.994341|0.994341|
+|2|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.449379|0.981386|0.852461|0.912391|0.912391|
+|3|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.449392|0.981406|0.852463|0.912401|0.912401|
+|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.370381|0.993314|0.790558|0.880414|0.880414|
+|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.057381|0.993824|0.756690|0.859195|0.859195|
+|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.015355|0.995442|0.723731|0.838115|0.838115|
+|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.007040|0.995635|0.710610|0.829316|0.829316|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|41074684|288483|898652|379567029627|288483 / 379567318110|898652 / 41973336|
+|2|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|12811693|2230481|29161643|379565087629|2230481 / 379567318110|29161643 / 41973336|
+|3|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|12811687|2228819|29161649|379565089291|2228819 / 379567318110|29161649 / 41973336|
+|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|9662476|536192|32310860|379566781918|536192 / 379567318110|32310860 / 41973336|
+|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|1244317|147956|40729019|379567170154|147956 / 379567318110|40729019 / 41973336|
+|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|325245|58578|41648091|379567259532|58578 / 379567318110|41648091 / 41973336|
+|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|148463|47859|41824873|379567270251|47859 / 379567318110|41824873 / 41973336|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|0.999993|2 / 379567318110 (0.000000%)|626 / 41973336 (0.001491%)|0.999997|0.999993|0.999993|
+|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|0.620065|416345 / 379567318110 (0.000110%)|31894218 / 41973336 (75.986855%)|0.600268|0.384195|0.480192|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0.999985|1.000000|0.999993|1.000000|0.000000|
+|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|0.960331|0.240131|0.999999|0.620065|0.999915|0.000085|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|0.999997|0.999993|0.999988|0.999985|0.999993|0.999993|
+|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|0.600268|0.384195|0.282504|0.237773|0.480214|0.480192|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|41972710|2|626|379567318108|2 / 379567318110|626 / 41973336|
+|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|10079118|416345|31894218|379566901765|416345 / 379567318110|31894218 / 41973336|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999991|1349800 / 379567318110 (0.000356%)|626 / 41973336 (0.001491%)|0.974915|0.984168|0.984289|
+|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|0.620065|888077 / 379567318110 (0.000234%)|31894218 / 41973336 (75.986855%)|0.587073|0.380771|0.469749|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.968843|0.999985|0.999996|0.999991|0.999996|0.000004|
+|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|0.919024|0.240131|0.999998|0.620065|0.999914|0.000086|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.974915|0.984168|0.993598|0.968829|0.984291|0.984289|
+|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|0.587073|0.380771|0.281759|0.235156|0.469773|0.469749|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|41972710|1349800|626|379565968310|1349800 / 379567318110|626 / 41973336|
+|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|10079118|888077|31894218|379566430033|888077 / 379567318110|31894218 / 41973336|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|HUNSPELL SPANISH LUCENE FILTER|416642|119847|351885|17877|2.051686%|5|890999|
+|Radixor|898026|288481|1061317|42637|4.893313%|21|916797|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **11 result rows**, **7 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.989429|276044 / 377860669765 (0.000073%)|885033 / 41863370 (2.114099%)|0.990385|0.986031|0.986056|
+|2|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.652720|2201196 / 377860669765 (0.000583%)|29076352 / 41863370 (69.455354%)|0.627946|0.449839|0.510450|
+|3|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.652720|2201196 / 377860669765 (0.000583%)|29076352 / 41863370 (69.455354%)|0.627946|0.449839|0.510450|
+|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.614999|531181 / 377860669765 (0.000141%)|32234855 / 41863370 (77.000144%)|0.583531|0.370163|0.466854|
+|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.514832|146613 / 377860669765 (0.000039%)|40621522 / 41863370 (97.033569%)|0.130949|0.057424|0.162875|
+|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.503877|57716 / 377860669765 (0.000015%)|41538714 / 41863370 (99.224487%)|0.037409|0.015370|0.081139|
+|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.501770|47148 / 377860669765 (0.000012%)|41715144 / 41863370 (99.645929%)|0.017379|0.007049|0.051824|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.993309|0.978859|0.999999|0.989429|0.999997|0.000003|
+|2|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.853138|0.305446|0.999994|0.652720|0.999917|0.000083|
+|3|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.853138|0.305446|0.999994|0.652720|0.999917|0.000083|
+|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.947717|0.229999|0.999999|0.614999|0.999913|0.000087|
+|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.894406|0.029664|1.000000|0.514832|0.999892|0.000108|
+|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.849058|0.007755|1.000000|0.503877|0.999890|0.000110|
+|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.758678|0.003541|1.000000|0.501770|0.999889|0.000111|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.990385|0.986031|0.981715|0.972447|0.986057|0.986056|
+|2|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.627946|0.449839|0.350441|0.290188|0.510478|0.510450|
+|3|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.627946|0.449839|0.350441|0.290188|0.510478|0.510450|
+|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.583531|0.370163|0.271053|0.227117|0.466876|0.466854|
+|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.130949|0.057424|0.036775|0.029561|0.162886|0.162875|
+|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.037409|0.015370|0.009672|0.007744|0.081145|0.081139|
+|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.017379|0.007049|0.004421|0.003537|0.051829|0.051824|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.986029|0.995464|0.993323|0.994392|0.994392|
+|2|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.449806|0.981469|0.852556|0.912482|0.912482|
+|3|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.449806|0.981469|0.852556|0.912482|0.912482|
+|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.370136|0.993362|0.790500|0.880396|0.880396|
+|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.057417|0.993866|0.756725|0.859234|0.859234|
+|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.015368|0.995484|0.723753|0.838145|0.838145|
+|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.007048|0.995676|0.710626|0.829341|0.829341|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|40978337|276044|885033|377860393721|276044 / 377860669765|885033 / 41863370|
+|2|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|12787018|2201196|29076352|377858468569|2201196 / 377860669765|29076352 / 41863370|
+|3|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|12787018|2201196|29076352|377858468569|2201196 / 377860669765|29076352 / 41863370|
+|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|9628515|531181|32234855|377860138584|531181 / 377860669765|32234855 / 41863370|
+|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|1241848|146613|40621522|377860523152|146613 / 377860669765|40621522 / 41863370|
+|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|324656|57716|41538714|377860612049|57716 / 377860669765|41538714 / 41863370|
+|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|148226|47148|41715144|377860622617|47148 / 377860669765|41715144 / 41863370|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 377860669765 (0.000000%)|0 / 41863370 (0.000000%)|1.000000|1.000000|1.000000|
+|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|0.619928|412198 / 377860669765 (0.000109%)|31822108 / 41863370 (76.014205%)|0.600000|0.383864|0.479978|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|0.960568|0.239858|0.999999|0.619928|0.999915|0.000085|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|0.600000|0.383864|0.282205|0.237519|0.480000|0.479978|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|41863370|0|0|377860669765|0 / 377860669765|0 / 41863370|
+|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|10041262|412198|31822108|377860257567|412198 / 377860669765|31822108 / 41863370|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999998|1255381 / 377860669765 (0.000332%)|0 / 41863370 (0.000000%)|0.976572|0.985228|0.985334|
+|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|0.619928|878949 / 377860669765 (0.000233%)|31822108 / 41863370 (76.014205%)|0.586905|0.380469|0.469606|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.970885|1.000000|0.999997|0.999998|0.999997|0.000003|
+|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|0.919512|0.239858|0.999998|0.619928|0.999913|0.000087|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.976572|0.985228|0.994038|0.970885|0.985335|0.985334|
+|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|0.586905|0.380469|0.281467|0.234926|0.469630|0.469606|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|41863370|1255381|0|377859414384|1255381 / 377860669765|0 / 41863370|
+|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|10041262|878949|31822108|377859790816|878949 / 377860669765|31822108 / 41863370|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|HUNSPELL SPANISH LUCENE FILTER|412747|118983|347768|17807|2.048262%|5|888962|
+|Radixor|885033|276044|979337|42403|4.877434%|21|914127|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `ES_ES`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/swedish.md

@@ -26,7 +26,7 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
@@ -42,11 +42,11 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `swedishRadixor` | 4.916 | 0.525 | 57.3 | 1.000 | Full Radixor dictionary patch-command stemmer. |
+| Radixor | `swedishRadixor` | 5.489 | 0.355 | 64.0 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Lucene SwedishMinimalStemFilter | `swedishLuceneSwedishMinimalStemFilter` | 4.453 | 0.530 | 51.9 | 0.906 | Minimal Swedish suffix reducer. |
+| Lucene SwedishMinimalStemFilter | `swedishLuceneSwedishMinimalStemFilter` | 4.630 | 0.130 | 54.0 | 0.843 | Minimal Swedish suffix reducer. |
-| Lucene SwedishLightStemFilter | `swedishLuceneSwedishLightStemFilter` | 4.523 | 0.151 | 52.8 | 0.920 | Light Swedish suffix stemmer. |
+| Lucene SwedishLightStemFilter | `swedishLuceneSwedishLightStemFilter` | 4.876 | 0.328 | 56.9 | 0.888 | Light Swedish suffix stemmer. |
-| Official Snowball direct | `snowballDirect[SWEDISH]` | 7.075 | 0.541 | 82.5 | 1.439 | Official Snowball generated Java stemmer; direct API. |
+| Official Snowball direct | `snowballDirect[SWEDISH]` | 7.517 | 0.072 | 87.7 | 1.370 | Official Snowball generated Java stemmer; direct API. |
-| Lucene SnowballFilter | `luceneSnowballFilter[SWEDISH]` | 9.379 | 0.056 | 109.4 | 1.908 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Lucene SnowballFilter | `luceneSnowballFilter[SWEDISH]` | 9.793 | 0.338 | 114.2 | 1.784 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
 
 ## Interpretation Notes
 
@@ -55,3 +55,366 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `SV_SE` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/sv_se/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.974636** among 5 deterministic stemmers. The runner-up is `SNOWBALL SWEDISH DIRECT` at 0.807534, a difference of 0.167101. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.974584** among 5 deterministic stemmers. The runner-up is `SNOWBALL SWEDISH DIRECT` at 0.807599, a difference of 0.166985. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **7 result rows**, **5 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.974636|24473 / 4812155436 (0.000509%)|19546 / 385342 (5.072377%)|0.939665|0.943246|0.943260|
+|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.807534|67105 / 4812155436 (0.001394%)|148325 / 385342 (38.491781%)|0.739832|0.687540|0.692339|
+|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.799307|64262 / 4812155436 (0.001335%)|154666 / 385342 (40.137333%)|0.736940|0.678180|0.684227|
+|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.796072|40227 / 4812155436 (0.000836%)|157161 / 385342 (40.784809%)|0.781991|0.698068|0.709491|
+|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.783685|45941 / 4812155436 (0.000955%)|166707 / 385342 (43.262089%)|0.757232|0.672808|0.684713|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.937292|0.949276|0.999995|0.974636|0.999991|0.000009|
+|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.779348|0.615082|0.999986|0.807534|0.999955|0.000045|
+|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.782117|0.598627|0.999987|0.799307|0.999955|0.000045|
+|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.850127|0.592152|0.999992|0.796072|0.999959|0.000041|
+|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.826360|0.567379|0.999990|0.783685|0.999956|0.000044|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.939665|0.943246|0.946855|0.892588|0.943265|0.943260|
+|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.739832|0.687540|0.642152|0.523856|0.692361|0.692339|
+|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.736940|0.678180|0.628098|0.513065|0.684249|0.684227|
+|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.781991|0.698068|0.630412|0.536179|0.709510|0.709491|
+|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.757232|0.672808|0.605321|0.506941|0.684733|0.684713|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.943241|0.992631|0.993395|0.993013|0.993013|
+|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.687518|0.984860|0.942685|0.963311|0.963311|
+|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.678157|0.985207|0.939659|0.961894|0.961894|
+|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.698048|0.988493|0.944582|0.966038|0.966038|
+|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.672787|0.986795|0.942303|0.964036|0.964036|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|365796|24473|19546|4812130963|24473 / 4812155436|19546 / 385342|
+|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|237017|67105|148325|4812088331|67105 / 4812155436|148325 / 385342|
+|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|230676|64262|154666|4812091174|64262 / 4812155436|154666 / 385342|
+|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|228181|40227|157161|4812115209|40227 / 4812155436|157161 / 385342|
+|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|218635|45941|166707|4812109495|45941 / 4812155436|166707 / 385342|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 4812155436 (0.000000%)|0 / 385342 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|385342|0|0|4812155436|0 / 4812155436|0 / 385342|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999995|47848 / 4812155436 (0.000994%)|0 / 385342 (0.000000%)|0.909640|0.941544|0.943152|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.889545|1.000000|0.999990|0.999995|0.999990|0.000010|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.909640|0.941544|0.975768|0.889545|0.943157|0.943152|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|385342|47848|0|4812107588|47848 / 4812155436|0 / 385342|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|19546|24473|23375|5767|5.878216%|5|104148|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **7 result rows**, **5 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.974584|24473 / 4789911577 (0.000511%)|19546 / 384563 (5.082652%)|0.939544|0.943132|0.943146|
+|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.807599|67105 / 4789911577 (0.001401%)|147975 / 384563 (38.478741%)|0.739645|0.687500|0.692274|
+|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.799355|64262 / 4789911577 (0.001342%)|154316 / 384563 (40.127625%)|0.736744|0.678122|0.684143|
+|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.795947|40227 / 4789911577 (0.000840%)|156939 / 384563 (40.809698%)|0.781694|0.697790|0.709212|
+|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.783598|45941 / 4789911577 (0.000959%)|166437 / 384563 (43.279515%)|0.756945|0.672575|0.684469|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.937167|0.949173|0.999995|0.974584|0.999991|0.000009|
+|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.779037|0.615213|0.999986|0.807599|0.999955|0.000045|
+|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.781800|0.598724|0.999987|0.799355|0.999954|0.000046|
+|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.849816|0.591903|0.999992|0.795947|0.999959|0.000041|
+|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.826025|0.567205|0.999990|0.783598|0.999956|0.000044|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.939544|0.943132|0.946748|0.892384|0.943151|0.943146|
+|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.739645|0.687500|0.642223|0.523810|0.692296|0.692274|
+|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.736744|0.678122|0.628142|0.512999|0.684165|0.684143|
+|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.781694|0.697790|0.630152|0.535851|0.709231|0.709212|
+|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.756945|0.672575|0.605126|0.506676|0.684489|0.684469|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.943127|0.992612|0.993378|0.992995|0.992995|
+|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.687478|0.984821|0.942695|0.963298|0.963298|
+|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.678100|0.985169|0.939661|0.961877|0.961877|
+|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.697770|0.988463|0.944528|0.965996|0.965996|
+|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.672553|0.986761|0.942265|0.964000|0.964000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|365017|24473|19546|4789887104|24473 / 4789911577|19546 / 384563|
+|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|236588|67105|147975|4789844472|67105 / 4789911577|147975 / 384563|
+|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|230247|64262|154316|4789847315|64262 / 4789911577|154316 / 384563|
+|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|227624|40227|156939|4789871350|40227 / 4789911577|156939 / 384563|
+|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|218126|45941|166437|4789865636|45941 / 4789911577|166437 / 384563|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 4789911577 (0.000000%)|0 / 384563 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|384563|0|0|4789911577|0 / 4789911577|0 / 384563|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999995|47848 / 4789911577 (0.000999%)|0 / 384563 (0.000000%)|0.909473|0.941433|0.943047|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.889346|1.000000|0.999990|0.999995|0.999990|0.000010|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.909473|0.941433|0.975720|0.889346|0.943051|0.943047|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|384563|47848|0|4789863729|47848 / 4789911577|0 / 384563|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|19546|24473|23375|5767|5.891848%|5|103921|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `SV_SE`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/ukrainian.md

@@ -26,11 +26,12 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
 | Radixor | 99.307% | 99.365% | 99.062% | Full Radixor dictionary patch-command stemmer. |
+| Lucene HunspellStemFilter | 86.815% | 83.759% | 99.866% | Benchmark-only Ukrainian Hunspell dictionary compared via Lucene HunspellStemFilter. |
 | Lucene MorfologikFilter | 92.362% | 90.637% | 99.732% | Dictionary-based path; Morfologik can emit multiple terms. |
 | Morfologik direct | 92.362% | 90.637% | 99.732% | Direct dictionary lookup; first returned stem is used for quality when no ranking weight is exposed. |
 
@@ -40,9 +41,10 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `ukrainianRadixor` | 0.605 | 0.056 | 47.4 | 1.000 | Full Radixor dictionary patch-command stemmer. |
+| Radixor | `ukrainianRadixor` | 0.682 | 0.057 | 53.5 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Morfologik direct | `ukrainianMorfologikDirect` | 8.106 | 0.040 | 635.7 | 13.408 | Direct Morfologik dictionary lookup; first returned stem is used for quality. |
+| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 43.527 | 1.207 | 3413.3 | 63.799 | Benchmark-only Ukrainian Hunspell dictionary compared via Lucene HunspellStemFilter. |
-| Lucene MorfologikFilter | `ukrainianLuceneMorfologikFilter` | 14.684 | 5.214 | 1151.5 | 24.287 | Dictionary-based Morfologik TokenFilter; may emit multiple terms. |
+| Morfologik direct | `ukrainianMorfologikDirect` | 8.680 | 0.073 | 680.7 | 12.723 | Direct Morfologik dictionary lookup; first returned stem is used for quality. |
+| Lucene MorfologikFilter | `ukrainianLuceneMorfologikFilter` | 14.575 | 0.248 | 1143.0 | 21.364 | Dictionary-based Morfologik TokenFilter; may emit multiple terms. |
 
 ## Interpretation Notes
 
@@ -51,3 +53,422 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `UK_UA` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/uk_ua/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.995343** among 4 deterministic stemmers. The runner-up is `UKRAINIAN LUCENE MORFOLOGIK FILTER` at 0.928768, a difference of 0.066575. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.995342** among 4 deterministic stemmers. The runner-up is `UKRAINIAN LUCENE MORFOLOGIK FILTER` at 0.928751, a difference of 0.066591. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **12 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.995343|880 / 101387550 (0.000868%)|608 / 65340 (0.930517%)|0.987406|0.988637|0.988632|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.928768|828 / 101387550 (0.000817%)|9308 / 65340 (14.245485%)|0.956896|0.917054|0.919223|
+|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.928646|828 / 101387550 (0.000817%)|9324 / 65340 (14.269972%)|0.956832|0.916912|0.919090|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.885793|794 / 101387550 (0.000783%)|14924 / 65340 (22.840526%)|0.933008|0.865139|0.871499|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.986588|0.990695|0.999991|0.995343|0.999985|0.000015|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.985438|0.857545|0.999992|0.928768|0.999900|0.000100|
+|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.985434|0.857300|0.999992|0.928646|0.999900|0.000100|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.984495|0.771595|0.999992|0.885793|0.999845|0.000155|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.987406|0.988637|0.989871|0.977529|0.988639|0.988632|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.956896|0.917054|0.880397|0.846814|0.919270|0.919223|
+|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.956832|0.916912|0.880190|0.846572|0.919137|0.919090|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.933008|0.865139|0.806475|0.762331|0.871568|0.871499|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.988630|0.997994|0.998266|0.998130|0.998130|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.917004|0.997990|0.971000|0.984310|0.984310|
+|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.916862|0.997990|0.970876|0.984246|0.984246|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.865063|0.998114|0.949804|0.973360|0.973360|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|64732|880|608|101386670|880 / 101387550|608 / 65340|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|56032|828|9308|101386722|828 / 101387550|9308 / 65340|
+|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|56016|828|9324|101386722|828 / 101387550|9324 / 65340|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|50416|794|14924|101386756|794 / 101387550|14924 / 65340|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 101387550 (0.000000%)|0 / 65340 (0.000000%)|1.000000|1.000000|1.000000|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.962151|122 / 101387550 (0.000120%)|4946 / 65340 (7.569636%)|0.982323|0.959732|0.960413|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|0.962029|122 / 101387550 (0.000120%)|4962 / 65340 (7.594123%)|0.982267|0.959599|0.960286|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|0.927570|326 / 101387550 (0.000322%)|9465 / 65340 (14.485767%)|0.962884|0.919443|0.922008|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.997984|0.924304|0.999999|0.962151|0.999950|0.000050|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|0.997983|0.924059|0.999999|0.962029|0.999950|0.000050|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|0.994199|0.855142|0.999997|0.927570|0.999903|0.000097|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.982323|0.959732|0.938156|0.922581|0.960438|0.960413|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|0.982267|0.959599|0.937954|0.922337|0.960310|0.960286|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|0.962884|0.919443|0.879752|0.850897|0.922053|0.922008|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|65340|0|0|101387550|0 / 101387550|0 / 65340|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|60394|122|4946|101387428|122 / 101387550|4946 / 65340|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|60378|122|4962|101387428|122 / 101387550|4962 / 65340|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|55875|326|9465|101387224|326 / 101387550|9465 / 65340|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999993|1490 / 101387550 (0.001470%)|0 / 65340 (0.000000%)|0.982084|0.988727|0.988782|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.962145|1368 / 101387550 (0.001349%)|4946 / 65340 (7.569636%)|0.966650|0.950323|0.950669|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|0.962023|1368 / 101387550 (0.001349%)|4962 / 65340 (7.594123%)|0.966592|0.950191|0.950541|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|0.927565|1271 / 101387550 (0.001254%)|9465 / 65340 (14.485767%)|0.950501|0.912349|0.914347|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.977705|1.000000|0.999985|0.999993|0.999985|0.000015|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.977850|0.924304|0.999987|0.962145|0.999938|0.000062|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|0.977845|0.924059|0.999987|0.962023|0.999938|0.000062|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|0.977759|0.855142|0.999987|0.927565|0.999894|0.000106|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.982084|0.988727|0.995460|0.977705|0.988789|0.988782|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.966650|0.950323|0.934539|0.905349|0.950700|0.950669|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|0.966592|0.950191|0.934337|0.905109|0.950571|0.950541|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|0.950501|0.912349|0.877142|0.838825|0.914398|0.914347|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|65340|1490|0|101386060|1490 / 101387550|0 / 65340|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|60394|1368|4946|101386182|1368 / 101387550|4946 / 65340|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|60378|1368|4962|101386182|1368 / 101387550|4962 / 65340|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|55875|1271|9465|101386279|1271 / 101387550|9465 / 65340|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|HUNSPELL UKRAINIAN LUCENE FILTER|5459|468|477|1322|9.280449%|6|15740|
+|UKRAINIAN LUCENE MORFOLOGIK FILTER|4362|706|540|2207|15.493155%|6|16937|
+|UKRAINIAN MORFOLOGIK DIRECT|4362|706|540|2207|15.493155%|6|16937|
+|Radixor|608|880|610|190|1.333801%|2|14435|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **12 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.995342|880 / 101259406 (0.000869%)|608 / 65324 (0.930745%)|0.987403|0.988634|0.988629|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.928751|828 / 101259406 (0.000818%)|9308 / 65324 (14.248974%)|0.956884|0.917032|0.919202|
+|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.928751|828 / 101259406 (0.000818%)|9308 / 65324 (14.248974%)|0.956884|0.917032|0.919202|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.885796|794 / 101259406 (0.000784%)|14920 / 65324 (22.839998%)|0.933007|0.865141|0.871500|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.986585|0.990693|0.999991|0.995342|0.999985|0.000015|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.985434|0.857510|0.999992|0.928751|0.999900|0.000100|
+|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.985434|0.857510|0.999992|0.928751|0.999900|0.000100|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.984492|0.771600|0.999992|0.885796|0.999845|0.000155|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.987403|0.988634|0.989868|0.977524|0.988636|0.988629|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.956884|0.917032|0.880367|0.846777|0.919249|0.919202|
+|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.956884|0.917032|0.880367|0.846777|0.919249|0.919202|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.933007|0.865141|0.806479|0.762334|0.871570|0.871500|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.988627|0.997992|0.998264|0.998128|0.998128|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.916982|0.997988|0.970978|0.984298|0.984298|
+|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.916982|0.997988|0.970978|0.984298|0.984298|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.865065|0.998113|0.949788|0.973351|0.973351|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|64716|880|608|101258526|880 / 101259406|608 / 65324|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|56016|828|9308|101258578|828 / 101259406|9308 / 65324|
+|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|56016|828|9308|101258578|828 / 101259406|9308 / 65324|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|50404|794|14920|101258612|794 / 101259406|14920 / 65324|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 101259406 (0.000000%)|0 / 65324 (0.000000%)|1.000000|1.000000|1.000000|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.962142|122 / 101259406 (0.000120%)|4946 / 65324 (7.571490%)|0.982318|0.959722|0.960404|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|0.962142|122 / 101259406 (0.000120%)|4946 / 65324 (7.571490%)|0.982318|0.959722|0.960404|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|0.927552|326 / 101259406 (0.000322%)|9465 / 65324 (14.489315%)|0.962874|0.919422|0.921988|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.997983|0.924285|0.999999|0.962142|0.999950|0.000050|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|0.997983|0.924285|0.999999|0.962142|0.999950|0.000050|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|0.994198|0.855107|0.999997|0.927552|0.999903|0.000097|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.982318|0.959722|0.938141|0.922562|0.960428|0.960404|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|0.982318|0.959722|0.938141|0.922562|0.960428|0.960404|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|0.962874|0.919422|0.879722|0.850861|0.922033|0.921988|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|65324|0|0|101259406|0 / 101259406|0 / 65324|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|60378|122|4946|101259284|122 / 101259406|4946 / 65324|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|60378|122|4946|101259284|122 / 101259406|4946 / 65324|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|55859|326|9465|101259080|326 / 101259406|9465 / 65324|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999993|1490 / 101259406 (0.001471%)|0 / 65324 (0.000000%)|0.982079|0.988724|0.988779|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.962136|1368 / 101259406 (0.001351%)|4946 / 65324 (7.571490%)|0.966642|0.950311|0.950657|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|0.962136|1368 / 101259406 (0.001351%)|4946 / 65324 (7.571490%)|0.966642|0.950311|0.950657|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|0.927547|1271 / 101259406 (0.001255%)|9465 / 65324 (14.489315%)|0.950487|0.912326|0.914325|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.977699|1.000000|0.999985|0.999993|0.999985|0.000015|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.977845|0.924285|0.999986|0.962136|0.999938|0.000062|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|0.977845|0.924285|0.999986|0.962136|0.999938|0.000062|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|0.977752|0.855107|0.999987|0.927547|0.999894|0.000106|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.982079|0.988724|0.995459|0.977699|0.988787|0.988779|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.966642|0.950311|0.934522|0.905326|0.950688|0.950657|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|0.966642|0.950311|0.934522|0.905326|0.950688|0.950657|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|0.950487|0.912326|0.877111|0.838787|0.914376|0.914325|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|65324|1490|0|101257916|1490 / 101259406|0 / 65324|
+|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|60378|1368|4946|101258038|1368 / 101259406|4946 / 65324|
+|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|60378|1368|4946|101258038|1368 / 101259406|4946 / 65324|
+|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|55859|1271|9465|101258135|1271 / 101259406|9465 / 65324|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|HUNSPELL UKRAINIAN LUCENE FILTER|5455|468|477|1321|9.279292%|6|15730|
+|UKRAINIAN LUCENE MORFOLOGIK FILTER|4362|706|540|2207|15.502950%|6|16928|
+|UKRAINIAN MORFOLOGIK DIRECT|4362|706|540|2207|15.502950%|6|16928|
+|Radixor|608|880|610|190|1.334645%|2|14426|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `UK_UA`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/languages/yiddish.md

@@ -25,11 +25,11 @@ Radixor stores the preferred transformation for each normalized dictionary word
 
 ## Accuracy
 
-Accuracy is computed from one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.
+Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
 
 | Stemmer | All exact | Changed exact | Root preserved | Note |
 | --- | ---: | ---: | ---: | --- |
-| Radixor | 98.930% | 98.343% | 100.000% | Radixor baseline in the Snowball-language comparison family. |
+| Radixor | 98.930% | 98.343% | 100.000% | Full Radixor dictionary patch-command stemmer. |
 | Lucene SnowballFilter | 2.837% | 2.558% | 3.346% | Lucene TokenFilter integration path around the Snowball algorithm. |
 | Official Snowball direct | 2.837% | 2.558% | 3.346% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
 
@@ -39,9 +39,9 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 
 | Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
 | --- | --- | ---: | ---: | ---: | ---: | --- |
-| Radixor | `radixor[YIDDISH]` | 0.236 | 0.004 | 85.1 | 1.000 | Radixor baseline for the Snowball-language comparison family. |
+| Radixor | `radixor[YIDDISH]` | 0.254 | 0.004 | 50.7 | 1.000 | Full Radixor dictionary patch-command stemmer. |
-| Official Snowball direct | `snowballDirect[YIDDISH]` | 1.432 | 0.193 | 515.7 | 6.058 | Official Snowball generated Java stemmer; direct API. |
+| Official Snowball direct | `snowballDirect[YIDDISH]` | 1.537 | 0.220 | 307.3 | 6.058 | Official Snowball generated Java stemmer; direct API. |
-| Lucene SnowballFilter | `luceneSnowballFilter[YIDDISH]` | 1.595 | 0.068 | 574.6 | 6.749 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
+| Lucene SnowballFilter | `luceneSnowballFilter[YIDDISH]` | 1.714 | 0.120 | 342.8 | 6.756 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
 
 ## Interpretation Notes
 
@@ -50,3 +50,346 @@ Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fo
 - Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 - Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
 - Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
+
+<!-- STEMMING-QUALITY:START -->
+
+## Stemming Quality
+
+Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `YI` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
+
+`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
+
+### Evaluation Scope and Key Findings
+
+The dictionary resource is `src/main/resources/yi/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
+
+- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.988241** among 3 deterministic stemmers. The runner-up is `SNOWBALL YIDDISH DIRECT` at 0.890988, a difference of 0.097253. This rank does not imply leadership in throughput or every secondary metric.
+- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.988241** among 3 deterministic stemmers. The runner-up is `SNOWBALL YIDDISH DIRECT` at 0.890988, a difference of 0.097253. This rank does not imply leadership in throughput or every secondary metric.
+### `ALL_WORDS`
+
+This mode contains **5 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.988241|195 / 6392909 (0.003050%)|149 / 6344 (2.348676%)|0.970881|0.972986|0.972965|
+|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.890988|1151 / 6392909 (0.018004%)|1382 / 6344 (21.784363%)|0.805624|0.796661|0.796600|
+|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.890988|1151 / 6392909 (0.018004%)|1382 / 6344 (21.784363%)|0.805624|0.796661|0.796600|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.969484|0.976513|0.999969|0.988241|0.999946|0.000054|
+|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.811713|0.782156|0.999820|0.890988|0.999604|0.000396|
+|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.811713|0.782156|0.999820|0.890988|0.999604|0.000396|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.970881|0.972986|0.975099|0.947393|0.972992|0.972965|
+|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.805624|0.796661|0.787894|0.662041|0.796798|0.796600|
+|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.805624|0.796661|0.787894|0.662041|0.796798|0.796600|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.972959|0.995691|0.996142|0.995917|0.995917|
+|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.796462|0.982919|0.962014|0.972354|0.972354|
+|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.796462|0.982919|0.962014|0.972354|0.972354|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|6195|195|149|6392714|195 / 6392909|149 / 6344|
+|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|4962|1151|1382|6391758|1151 / 6392909|1382 / 6344|
+|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|4962|1151|1382|6391758|1151 / 6392909|1382 / 6344|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 6392909 (0.000000%)|0 / 6344 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|6344|0|0|6392909|0 / 6392909|0 / 6344|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999970|389 / 6392909 (0.006085%)|0 / 6344 (0.000000%)|0.953240|0.970253|0.970653|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.942225|1.000000|0.999939|0.999970|0.999939|0.000061|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.953240|0.970253|0.987885|0.942225|0.970683|0.970653|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|6344|389|0|6392520|389 / 6392909|0 / 6344|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|149|195|194|89|2.487423%|3|3676|
+
+### `LOWERCASE_GROUPS_ONLY`
+
+This mode contains **5 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
+
+#### `PRIMARY_OUTPUT` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.988241|195 / 6392909 (0.003050%)|149 / 6344 (2.348676%)|0.970881|0.972986|0.972965|
+|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.890988|1151 / 6392909 (0.018004%)|1382 / 6344 (21.784363%)|0.805624|0.796661|0.796600|
+|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.890988|1151 / 6392909 (0.018004%)|1382 / 6344 (21.784363%)|0.805624|0.796661|0.796600|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.969484|0.976513|0.999969|0.988241|0.999946|0.000054|
+|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.811713|0.782156|0.999820|0.890988|0.999604|0.000396|
+|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.811713|0.782156|0.999820|0.890988|0.999604|0.000396|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.970881|0.972986|0.975099|0.947393|0.972992|0.972965|
+|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.805624|0.796661|0.787894|0.662041|0.796798|0.796600|
+|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.805624|0.796661|0.787894|0.662041|0.796798|0.796600|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|0.972959|0.995691|0.996142|0.995917|0.995917|
+|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.796462|0.982919|0.962014|0.972354|0.972354|
+|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.796462|0.982919|0.962014|0.972354|0.972354|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|PRIMARY_OUTPUT|6195|195|149|6392714|195 / 6392909|149 / 6344|
+|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|4962|1151|1382|6391758|1151 / 6392909|1382 / 6344|
+|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|4962|1151|1382|6391758|1151 / 6392909|1382 / 6344|
+
+</details>
+
+#### `ANY_CANDIDATE` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|0 / 6392909 (0.000000%)|0 / 6344 (0.000000%)|1.000000|1.000000|1.000000|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ANY_CANDIDATE|6344|0|0|6392909|0 / 6392909|0 / 6344|
+
+</details>
+
+#### `ALL_CANDIDATES` ranking
+
+<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
+
+| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.999970|389 / 6392909 (0.006085%)|0 / 6344 (0.000000%)|0.953240|0.970253|0.970653|
+
+</div>
+
+<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
+
+| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.942225|1.000000|0.999939|0.999970|0.999939|0.000061|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
+
+| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|0.953240|0.970253|0.987885|0.942225|0.970683|0.970653|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
+
+| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
+|---:|---|---|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
+
+</details>
+
+<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
+
+| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
+|---:|---|---|---:|---:|---:|---:|---:|---:|
+|1|Radixor|ALL_CANDIDATES|6344|389|0|6392520|389 / 6392909|0 / 6344|
+
+</details>
+
+#### Multi-output analysis
+
+Alternative candidates are capability analyses, not replacements for the deterministic comparison.
+
+| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
+|---|---:|---:|---:|---:|---:|---:|---:|
+|Radixor|149|195|194|89|2.487423%|3|3676|
+
+### Output Policies and Metric Definitions
+
+`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
+
+For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
+
+- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
+- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
+- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
+- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
+- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
+- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
+- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
+- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
+- Jaccard index: `TP / (TP + FP + FN)`.
+- Fowlkes–Mallows index: `sqrt(precision * recall)`.
+- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
+- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
+
+Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
+
+### Provenance
+
+- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
+- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Evaluation command: `./gradlew stemmingQuality`
+- Dictionary language: `YI`
+- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
+- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
+- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
+
+<!-- STEMMING-QUALITY:END -->

docs/benchmarks/reference/candidates.md

@@ -4,20 +4,28 @@ Implemented benchmark methods are documented on the per-language pages under [La
 
 ## Included Candidate Families
 
-The current benchmark pages include Radixor baselines, Lucene language filters where the language matches a bundled Radixor resource, Lucene Stempel and Morfologik paths where applicable, official Snowball Java stemmers where same-language comparison is available, and selected English-specific non-Lucene baselines such as OpenNLP Porter and Paice/Husk Lancaster.
+The current benchmark pages include Radixor baselines, Lucene language filters where the language matches a bundled Radixor resource, Lucene Stempel and Morfologik paths where applicable, official Snowball Java stemmers where same-language comparison is available, benchmark-only CISTEM German stemmer evaluation, benchmark-only Hunspell comparisons, and selected English-specific non-Lucene baselines such as OpenNLP Porter and Paice/Husk Lancaster.
+
+Benchmark-only Hunspell comparisons use bundled benchmark dictionaries and the Lucene HunspellStemFilter adapter over the selected language token streams.
+The CISTEM candidate is implemented in `src/jmh/java/org/egothor/stemmer/benchmark/Cistem.java` and follows the original MIT-licensed upstream implementation from Leonie Weissweiler's CISTEM project.
+CISTEM German gold-standard files are not vendored in this repository. The Gradle JMH resource preparation tasks download `goldstandard1.txt` and `goldstandard2.txt` from the upstream CISTEM repository into generated build resources.
 
 Direct stemmer APIs and Lucene TokenFilter paths are documented separately on language pages. TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
 
+For the benchmark refresh used in this documentation build:
+
+- Hunspell families are included in `HunspellStemmerComparisonBenchmark` (speed) and `HunspellStemmerComparisonBenchmarkQuality` (quality for all benchmark languages in this corpus). The legacy
+  `EnglishHunspellStemmerComparisonBenchmarkQuality` result is retained for continuity.
+- CISTEM quality is present in the published per-language results under `GERMAN_CISTEM`. CISTEM speed is present as `germanCistem` in `MultiLanguageStemmerComparisonBenchmark`.
+
 ## Evaluated But Skipped Candidates
 
 | Candidate | Language | Link/source | Reason skipped |
 | --- | --- | --- | --- |
 | Lucene Arabic, Bulgarian, Bengali, Sorani, Greek, Galician, Hindi, Indonesian, Latvian, Telugu filters | Various | `lucene-analysis-common` | No bundled same-language Radixor resource in this repository snapshot. |
 | Lucene analyzer-only paths | Multiple | Lucene analyzers | Full analyzers mix tokenization, stop-word handling, and other behavior; direct filters are used where available. |
-| Lucene HunspellStemFilter | Multiple | `lucene-analysis-common` | Requires external Hunspell dictionaries not resolved as benchmark-only resources here. |
 | Lucene StemmerOverrideFilter | Multiple | `lucene-analysis-common` | Override map facility, not a stemmer algorithm. |
 | Additional Snowball Lovins | English | Official Snowball Java distribution | No Lovins Java stemmer was present in the selected Snowball Java distribution. |
 | Lemur Project Krovetz Stemmer | English | Lemur Project | Lucene KStem represents the Krovetz-style path without adding separate dependency and license risk. |
 | Smile Lancaster / Paice-Husk | English | Smile NLP | Smile is large for one stemmer; Paice/Husk is included through a smaller benchmark-only generated path. |
-| CISTEM German stemmer | German | `https://github.com/LeonieWeissweiler/CISTEM` | Clean benchmark-only Java integration was not completed in this phase. |
 | `stemmerEval` reference repository | Multiple | `https://github.com/endredy/stemmerEval` | Used only as a candidate reference; no code or data copied. |

docs/benchmarks/reference/english-coverage.md

@@ -6,16 +6,16 @@ This benchmark is the clearest demonstration of the Radixor quality/speed envelo
 
 | Used rows | Actual row ratio | All exact | Changed exact | Root preserved | Speed ms/op | Error ms | ns/token |
 | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| 100% | 100.000% | 97.478% | 97.197% | 97.552% | 23.113 | 7.065 | 109.8 |
+| 100% | 100.000% | 97.478% | 97.197% | 97.552% | 28.578 | 7.571 | 135.8 |
-| 90% | 90.000% | 97.047% | 94.913% | 97.613% | 21.270 | 9.914 | 101.0 |
+| 90% | 90.000% | 97.047% | 94.913% | 97.613% | 26.612 | 9.227 | 126.4 |
-| 80% | 80.000% | 96.635% | 92.768% | 97.661% | 19.170 | 6.609 | 91.1 |
+| 80% | 80.000% | 96.635% | 92.768% | 97.661% | 23.331 | 8.106 | 110.8 |
-| 70% | 70.000% | 96.209% | 90.565% | 97.705% | 20.857 | 6.734 | 99.1 |
+| 70% | 70.000% | 96.209% | 90.565% | 97.705% | 22.362 | 1.957 | 106.2 |
-| 60% | 60.000% | 95.750% | 88.384% | 97.703% | 14.975 | 1.215 | 71.1 |
+| 60% | 60.000% | 95.750% | 88.384% | 97.703% | 16.497 | 2.026 | 78.4 |
-| 50% | 50.000% | 95.262% | 86.107% | 97.690% | 15.249 | 1.078 | 72.4 |
+| 50% | 50.000% | 95.262% | 86.107% | 97.690% | 16.035 | 0.986 | 76.2 |
-| 40% | 40.000% | 94.753% | 83.855% | 97.643% | 15.323 | 2.340 | 72.8 |
+| 40% | 40.000% | 94.753% | 83.855% | 97.643% | 16.459 | 0.664 | 78.2 |
-| 30% | 30.000% | 94.208% | 81.651% | 97.537% | 16.778 | 2.643 | 79.7 |
+| 30% | 30.000% | 94.208% | 81.651% | 97.537% | 19.566 | 0.758 | 92.9 |
-| 20% | 20.000% | 93.633% | 79.366% | 97.416% | 18.929 | 3.241 | 89.9 |
+| 20% | 20.000% | 93.633% | 79.366% | 97.416% | 14.616 | 0.487 | 69.4 |
-| 10% | 10.000% | 92.868% | 76.516% | 97.204% | 19.124 | 1.883 | 90.9 |
+| 10% | 10.000% | 92.868% | 76.516% | 97.204% | 18.093 | 3.147 | 86.0 |
 
 ## Column Meanings
 

docs/benchmarks/reference/environment.md

@@ -4,11 +4,11 @@ The values below are environment-specific and must not be read as universal perf
 
 | Item | Value |
 | --- | --- |
-| Benchmark date | 2026-07-03 |
+| Benchmark date | 2026-07-06 (Europe/Prague) |
-| Focused comparison command family | JMH jar runs limited to `EnglishStemmerComparisonBenchmark`, `MultiLanguageStemmerComparisonBenchmark`, and `SnowballLanguageStemmerComparisonBenchmark`; Radixor exact-root metrics were recomputed deterministically against the same contracted loaders |
+| Focused comparison command family | `./gradlew jmh -Pjmh.includes='.*StemmerComparisonBenchmark.*' --no-daemon` |
 | English coverage command | `./gradlew jmh -Pjmh.includes='.*EnglishRadixorDictionaryCoverageBenchmark.*' --no-daemon` |
-| Speed result reports | `build/reports/jmh/contracted/english-comparison.csv`, `multilanguage-speed.csv`, `snowball-language-speed.csv` |
+| Speed result reports | `build/reports/jmh/stemmer-comparison-2026-07-06.csv`, `build/reports/jmh/stemmer-comparison-2026-07-06.txt`, `build/reports/jmh/english-coverage-2026-07-06.csv`, and `build/reports/jmh/english-coverage-2026-07-06.txt` |
-| Accuracy result reports | Deterministic Radixor exact-root pass over bundled dictionaries; non-Radixor quality rows retained from the existing published quality suite |
+| Accuracy result reports | `build/reports/jmh/stemmer-comparison-2026-07-06.csv`, `build/reports/jmh/english-coverage-2026-07-06.csv`, and deterministic Radixor exact-root accounting over the same bundled language corpora |
 | Final comparison JMH scope | Stemmer comparison benchmarks only; internal `FrequencyTrie*` microbenchmarks were not run |
 | Coverage JMH scope | English Radixor dictionary coverage benchmark only |
 | JMH version | 1.37 |
@@ -16,15 +16,19 @@ The values below are environment-specific and must not be read as universal perf
 | Score unit | `ns/op` |
 | Speed warmup | 3 iterations, 1 s each |
 | Speed measurement | 5 iterations, 1 s each |
-| Accuracy warmup | none for deterministic exact-root accounting |
+| Accuracy warmup | 3 JMH warmup iterations were applied by the Gradle invocation; timing scores from quality methods are not interpreted |
-| Accuracy measurement | 1 deterministic measurement iteration; counters only, not speed interpretation |
+| Accuracy measurement | 5 JMH measurement samples; documentation uses deterministic auxiliary counter ratios from the same report |
 | Fork count in generated report files | 1 |
 | Default fork policy for accuracy-only benchmark classes | `@Fork(0)` for future default runs because accuracy counters are deterministic and not interpreted as speed |
 | Thread count | 1 |
-| JVM reported by JMH | OpenJDK 64-Bit Server VM, 25.0.3+9 |
+| JVM reported by JMH | JDK 25.0.3, OpenJDK 64-Bit Server VM, 25.0.3+9 |
+| Java runtime | OpenJDK Runtime Environment, Red Hat build 25.0.3+9 |
 | JVM invoker | `/usr/lib/jvm/java-25-openjdk/bin/java` |
-| Operating system | Linux 7.0.13-200.fc44.x86_64 |
+| Operating system | Fedora Linux 44 (MATE-Compiz) |
-| CPU | AMD Ryzen 5 7600 6-Core Processor |
+| Kernel | Linux 7.0.12-201.fc44.x86_64 |
+| Architecture | x86_64 |
+| CPU | AMD Ryzen 5 8600G w/ Radeon 760M Graphics |
+| Physical cores | 6 |
 | Logical CPUs | 12 |
 
 ## Contracted Trie Baseline
@@ -35,12 +39,10 @@ All Radixor rows in the refreshed benchmark tables use contracted compiled patch
 
 Generated local report files for this benchmark update:
 
-- `build/reports/jmh/contracted/english-comparison.csv`
+- `build/reports/jmh/stemmer-comparison-2026-07-06.csv`
-- `build/reports/jmh/contracted/english-comparison.txt`
+- `build/reports/jmh/stemmer-comparison-2026-07-06.txt`
-- `build/reports/jmh/contracted/multilanguage-speed.csv`
+- `build/reports/jmh/english-coverage-2026-07-06.csv`
-- `build/reports/jmh/contracted/multilanguage-speed.txt`
+- `build/reports/jmh/english-coverage-2026-07-06.txt`
-- `build/reports/jmh/contracted/snowball-language-speed.csv`
-- `build/reports/jmh/contracted/snowball-language-speed.txt`
 
 JMH TXT and CSV reports are still published as benchmark artifacts. They are not converted into a Porter speed badge.
 

docs/benchmarks/reference/linguistic-quality.md

@@ -0,0 +1,82 @@
+# Linguistic Quality Methodology
+
+This evaluation measures agreement between the relation predicted by a stemmer and the gold-standard relation represented by Radixor dictionary groups. It does not require a generated stem to equal one predetermined lemma string. Runtime performance and linguistic quality are separate measurements.
+
+## Scope and fair-comparison rules
+
+The authoritative Radixor language universe is the reconciled set of `stemmer.gz` resources under `src/main/resources` and `StemmerPatchTrieLoader.Language`. Radixor is evaluated for every reconciled language. A third-party adapter is evaluated only for languages supported by its tested implementation and having a compatible Radixor dictionary; unsupported combinations are absent rather than assigned zero quality.
+
+Within one language and dictionary mode, every adapter receives the same original included forms. Exact duplicates are removed only within one dictionary row. Identical surface forms in different rows remain distinct entries. Candidate strings use exact `String.equals`, with no evaluation-only lowercasing, normalization, accent removal, or gold-label-aware selection. Adapter preprocessing and lifecycle match the JMH comparison path.
+
+## Gold-standard pairs
+
+Every usable dictionary row is a gold-standard equivalence group. An unordered pair from the same row is positive; a pair from different rows is negative. For group size `n`, `C2(n) = n * (n - 1) / 2`.
+
+- `TP = underPossiblePairs - underErrorPairs`: same-group pairs correctly related.
+- `FN = underErrorPairs`: same-group pairs incorrectly separated.
+- `FP = overErrorPairs`: different-group pairs incorrectly related.
+- `TN = overPossiblePairs - overErrorPairs`: different-group pairs correctly separated.
+
+Under-stemming is the false-negative relation among same-group pairs. Over-stemming is the false-positive relation among different-group pairs. Their percentages use different denominators and must not be added or averaged without an explicitly defined composite.
+
+## Dictionary-processing modes
+
+- `ALL_WORDS` includes every valid group and preserves every original form.
+- `LOWERCASE_GROUPS_ONLY` excludes an entire group if any Unicode code point is uppercase or titlecase. Retained forms are not converted to lowercase. Digits, punctuation, combining marks, and characters without case distinctions do not exclude a group by themselves.
+
+## Output policies
+
+`PRIMARY_OUTPUT` uses the adapter's deterministic primary stem. It defines a strict predicted partition and is the principal direct comparison between implementations.
+
+`ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound. Same-group pairs succeed when candidate sets intersect. Different-group pairs avoid an error whenever a non-colliding candidate selection exists. Selection may differ between pairs, so this policy is not deterministic runtime behaviour and may not correspond to one globally realizable assignment.
+
+`ALL_CANDIDATES` treats every returned candidate as active. Two forms are related when their candidate sets intersect. Alternatives can recover same-group relationships while introducing cross-group collisions. This overlapping relation need not be transitive or form a partition.
+
+Candidate-aware policies are reported as capability analyses. They are not mixed into the principal `PRIMARY_OUTPUT` ranking.
+
+## Relation metrics
+
+Undefined denominators produce `n/a`, never zero, `NaN`, or infinity. Metrics are calculated from unrounded raw counts and displayed with six decimals.
+
+| Metric | Formula | Range and interpretation | Sensitivity and applicability |
+| --- | --- | --- | --- |
+| Under-stemming rate | `FN / (TP + FN)` | `[0, 1]`; lower is better. False-negative rate over same-group pairs. | Sensitive to splitting large gold groups. All policies. |
+| Over-stemming rate | `FP / (TN + FP)` | `[0, 1]`; lower is better. False-positive rate over different-group pairs. | The denominator is usually very large. All policies. |
+| Precision | `TP / (TP + FP)` | `[0, 1]`; higher is better. Fraction of predicted relations that are gold-positive. | Penalizes over-stemming. All policies, with oracle-assisted interpretation for `ANY_CANDIDATE`. |
+| Recall | `TP / (TP + FN)` | `[0, 1]`; higher is better. Fraction of gold-positive pairs recovered. | Equivalent to one minus the under-stemming rate. All policies. |
+| Specificity | `TN / (TN + FP)` | `[0, 1]`; higher is better. Fraction of negative pairs separated. | Sensitive to cross-group collisions. All policies. |
+| Balanced accuracy | `(recall + specificity) / 2` | `[0, 1]`; higher is better. Equal weight for positive and negative classes. | Primary navigation metric; less dominated by TN than ordinary accuracy, but not uniquely authoritative. |
+| Pairwise accuracy | `(TP + TN) / (TP + TN + FP + FN)` | `[0, 1]`; higher is better. | Can be dominated by the very large TN class and is not the default ranking metric. |
+| Pairwise error rate | `(FP + FN) / (TP + TN + FP + FN)` | `[0, 1]`; lower is better. | Also sensitive to the number of negative pairs. |
+| F0.5 | `1.25 TP / (1.25 TP + 0.25 FN + FP)` | `[0, 1]`; higher is better. | Gives greater weight to precision and over-stemming avoidance. |
+| F1 | `2 TP / (2 TP + FN + FP)` | `[0, 1]`; higher is better. | Equal precision/recall emphasis. |
+| F2 | `5 TP / (5 TP + 4 FN + FP)` | `[0, 1]`; higher is better. | Gives greater weight to recall and under-stemming avoidance. |
+| Jaccard | `TP / (TP + FP + FN)` | `[0, 1]`; higher is better. | Excludes TN. All policies. |
+| Fowlkes–Mallows | `sqrt(precision * recall)` | `[0, 1]`; higher is better. | Geometric balance of precision and recall. All policies. |
+| MCC | `(TP TN - FP FN) / sqrt((TP+FP)(TP+FN)(TN+FP)(TN+FN))` | `[-1, 1]`; higher is better. Uses all four counts. | Informative under imbalance; undefined for a zero product denominator. All policies with policy-specific interpretation. |
+
+The general F-beta formula is `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`.
+
+## Partition-only metrics
+
+These metrics apply only to `PRIMARY_OUTPUT`. Candidate relations are not forced into artificial partitions.
+
+- Adjusted Rand Index is the Rand agreement corrected for agreement expected from the gold/predicted contingency-table marginals. Its usual range is `[-1, 1]`, with `1` indicating identical partitions.
+- Homogeneity is `1 - H(gold | predicted) / H(gold)`, in `[0, 1]`; each predicted cluster ideally contains one gold group.
+- Completeness is `1 - H(predicted | gold) / H(predicted)`, in `[0, 1]`; each gold group ideally maps to one predicted cluster.
+- V-measure is the harmonic mean of homogeneity and completeness, in `[0, 1]`.
+- Normalized mutual information uses arithmetic-mean entropy normalization: `MI / ((H(gold) + H(predicted)) / 2)`, in `[0, 1]` under this implementation.
+
+Entropy zero cases follow the evaluator's explicit perfect/undefined conventions. Language tables render inapplicable candidate-policy values as `n/a`.
+
+## Aggregation and ranking
+
+Macro metrics average defined per-language values, giving each language equal weight. Micro metrics sum TP, FP, FN, and TN before calculating a metric. Cross-stemmer aggregate comparisons require the exact common supported-language intersection; unsupported languages are not zero-filled.
+
+Language tables sort by unrounded balanced accuracy, then MCC, F1, over-stemming rate, over-stemming error count, under-stemming rate, stemmer name, and stable policy order. Display rounding never controls rank.
+
+Multiple metrics and Pearson/Spearman correlation datasets are published because metric suitability and correlation remain analytical questions. Strong correlation does not establish equivalence.
+
+## Limitations
+
+Dictionary groups encode the available annotation, not every linguistic distinction. Homographs may occur in different groups, singleton rows contribute no under-stemming pair, and group size affects pair counts. `ANY_CANDIDATE` is optimistic; `ALL_CANDIDATES` measures an overlapping graph; neither is a deterministic global assignment. Results characterize the tested versions, adapters, dictionaries, and preprocessing, not every deployment or domain.

docs/benchmarks/reference/methodology.md

@@ -21,11 +21,9 @@ timePerChangedTokenNs = JMH score ns/op / changedTimingTokenCount
 
 This is necessary because Radixor dictionaries have different token counts by language.
 
-## Quality And Search Interpretation
+## Exact-root quality and interpretation
 
-Radixor speed must be interpreted together with exact-root quality. A slower Radixor row must not be read as a simple performance weakness when Radixor is also the row with accuracy close to 100% and competing stemmers are much lower.
+Runtime and exact-root agreement must be interpreted separately. Light, minimal, possessive, and aggressive rule-based implementations deliberately address different scopes and may achieve lower latency by performing fewer transformations. A throughput advantage does not establish higher linguistic quality, and higher dictionary agreement does not establish lower operational cost.
-
-Many fast light, minimal, possessive, or aggressive rule-based stemmers are fast because they do much less linguistic work. The measured Radixor cost buys dictionary-trained precision, and that precision is what improves search quality when queries and indexed text are reduced to the same intended roots.
 
 The [English dictionary coverage benchmark](english-coverage.md) shows this operating curve explicitly: contracted tries reduce lookup cost in uniform regions, while reduced dictionary coverage still lowers changed-form precision.
 
@@ -56,4 +54,6 @@ rootPreservedPercent = rootPreservedMatches / rootEvaluatedTokens * 100
 
 Morfologik can emit multiple terms for one input token. The quality benchmark uses the first emitted term for exact-root accounting when no ranking weight is exposed. Throughput benchmarks for Morfologik TokenFilter paths consume all emitted terms.
 
-Quality reports intentionally use one deterministic measurement iteration without warmup, because exact-root agreement is not a timing metric and repeated precision passes would only duplicate the same counters.
+Quality reports use JMH auxiliary counter rows. Exact-root accounting is deterministic for a fixed corpus and stemmer, so repeated measurement samples duplicate the same counters; documentation uses the counter ratios and does not interpret quality benchmark timing scores.
+
+Pairwise over-stemming, under-stemming, candidate-aware policies, balanced accuracy, and partition comparison are a separate analytical evaluation. See [Linguistic Quality Methodology](linguistic-quality.md); exact-root accuracy must not be interpreted as the complement of pairwise under-stemming.

docs/benchmarks/reference/reproducibility.md

@@ -0,0 +1,61 @@
+# Reproducibility and Raw Data
+
+## Published quality snapshot
+
+- Machine-readable CSV: [stemming-quality.csv](../data/stemming-quality.csv)
+- SHA-256 record: [stemming-quality.sha256](../data/stemming-quality.sha256)
+- SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
+- Complete scenarios: 308
+- Authoritative language universe: 20 languages
+- Language-page scenarios: 302 across 19 existing benchmark pages
+
+The six remaining scenarios are the three Radixor policies in two modes for `HE_IL`. Hebrew is present in the complete result snapshot but has no existing language benchmark page.
+
+The CSV contains raw TP, FP, FN, and TN counts; raw over/under numerators and denominators; candidate statistics; relation metrics; and partition-only metrics. Documentation is regenerated from this file rather than manually transcribed.
+
+## Commands
+
+```bash
+./gradlew stemmingQuality
+./gradlew publishStemmingQualityDocumentation
+./gradlew verifyStemmingQualityDocumentation
+./gradlew test
+mkdocs build --strict
+```
+
+`stemmingQuality` performs the expensive complete evaluation and is intentionally not attached to `test` or `check`. It prepares JMH third-party dependencies automatically and writes:
+
+- `build/reports/stemming-quality/stemming-quality.csv`
+- `build/reports/stemming-quality/stemming-quality.md`
+- `build/reports/stemming-quality/metric-correlations-pearson.csv`
+- `build/reports/stemming-quality/metric-correlations-spearman.csv`
+
+Audit mode is enabled with `-PstemmingQualityAudit=true`. Language, stemmer, dictionary-mode, output-policy, and ranking filters are documented on the central [stemming-quality page](../../stemming-quality.md). Filtered reports use separate filenames and cannot be accepted as publication sources.
+
+`publishStemmingQualityDocumentation` validates the complete build CSV, copies a versioned documentation snapshot, and replaces only marked generated sections. `verifyStemmingQualityDocumentation` re-renders from the checked-in snapshot and fails on changed values, ordering, missing pages, duplicate keys, arithmetic inconsistencies, policy violations, or stale sections.
+
+## Performance benchmark reproduction
+
+The JMH comparison command family is:
+
+```bash
+./gradlew jmh -Pjmh.includes='.*StemmerComparisonBenchmark.*' --no-daemon
+```
+
+The exact JMH configuration, hardware, operating system, and JDK captured for the published performance tables are listed in [Environment and reports](environment.md). Quality and performance reports are separate datasets and are not combined into an undocumented scalar.
+
+## Recorded and unavailable provenance
+
+The performance documentation records its 2026-07-06 environment, JDK 25.0.3, operating system, and hardware. The quality CSV records the evaluated identifiers and counts but does not embed the Radixor Git revision, generation date, JDK, operating system, dictionary content hash, or immutable upstream revisions for every downloaded source. These fields are explicitly unavailable for this snapshot and are not reconstructed from filesystem timestamps.
+
+Dependency versions that are reproducible from repository configuration include Apache Lucene 10.5.0, Morfologik 2.1.9, the Ukrainian dictionary artifact 4.9.1, and JMH 1.37. Other upstream branches or downloaded dictionary revisions should be pinned and embedded in a future result schema.
+
+## Correlation and audit data
+
+Pearson and Spearman files are generated from unrounded metric values in cohorts separated by dictionary mode and output policy. A missing coefficient means too few observations, undefined input, or zero variance. Correlation is descriptive and does not demonstrate that two metrics are scientifically interchangeable.
+
+Audit reports preserve original multilingual forms and identify high-contributing dictionary groups. They are build artifacts rather than checked-in publication data because of their size. No documentation value is manually altered after generation.
+
+## JMH badge compatibility
+
+The quality documentation generator does not invoke JMH, change JMH result formats, or modify badge tooling. Existing JMH result paths and historical badge-compatible inputs remain independent. The repository currently publishes coverage and mutation badge metadata and retains JMH TXT/CSV artifacts as documented in [Environment and reports](environment.md).

docs/benchmarks/reference/tested-stemmers.md

@@ -0,0 +1,29 @@
+# Tested Stemmer Inventory
+
+The JMH adapter registry is authoritative for evaluated implementations and language mappings. Names below describe the implementation actually invoked, not an abstract algorithm in every possible implementation. Unsupported language combinations are omitted rather than scored as failures.
+
+| Family or implementation | Upstream / attribution | Tested version or revision | Evaluated scope | Output capability and adapter behaviour | Interpretation notes |
+| --- | --- | --- | --- | --- | --- |
+| Radixor | Egothor / Radixor project | Current repository revision; exact revision was not embedded in the quality CSV | All 20 reconciled Radixor dictionary languages; 19 have benchmark pages | Deterministic preferred patch via `get`; ranked distinct alternatives via `getAll`; primary is always included | Dictionary-derived compiled patch trie. Quality depends on dictionary coverage and annotation. |
+| Apache Lucene language stem filters | Apache Lucene project | 10.5.0 | Adapter-declared language-specific subsets | TokenFilter lifecycle and language normalization match JMH; normally single-output | Light, minimal, possessive, and language stem filters deliberately implement different scopes. Narrow scope is not a defect. |
+| Apache Lucene SnowballFilter | Apache Lucene project using Snowball algorithms | Lucene 10.5.0 | Snowball-supported subset of Radixor languages | Single primary token emitted through the Lucene TokenFilter path | Includes TokenStream overhead and required normalization. |
+| Official Snowball Java | Snowball project | Repository preparation downloads the configured upstream Java distribution; an immutable revision was not recorded in the quality CSV | Same-language adapter subset | Direct generated Java API; single output | Rule-based suffix algorithms provide broad baselines rather than dictionary-root guarantees. |
+| Lucene Stempel | Apache Lucene / Polish stemming tables | Lucene 10.5.0 | Polish | Direct and TokenFilter paths where registered; single primary output | Table-driven Polish implementation. |
+| Morfologik | Morfologik project; Lucene integration by Apache Lucene | Morfologik 2.1.9, Lucene integration 10.5.0; Ukrainian dictionary artifact 4.9.1 | Registered Polish and Ukrainian paths | Deterministic first lemma for primary comparison; all distinct lemma strings for candidate policies | Several analyses may share a lemma and are deduplicated by exact string equality. |
+| Hunspell via Lucene | Hunspell dictionaries from the `wooorm/dictionaries` repository; adapter by Apache Lucene | Lucene 10.5.0; dictionary repository revision was not recorded | Configured German, English, Spanish, French, Dutch, Polish, and Ukrainian dictionaries | First emitted stem is primary; all distinct stems at the token position are candidates | Dictionary content and affix rules differ by language. |
+| CISTEM | Leonie Weissweiler, CISTEM project | Upstream `master` source path used by preparation; immutable commit not recorded | German | Single output | German stemming algorithm; benchmark-only implementation and gold-standard preparation remain under JMH infrastructure. |
+| OpenNLP Porter | Apache OpenNLP project | Version resolved by `gradle/opennlp-benchmarks.gradle` and `gradle.lockfile` | English | Direct single output | Porter-family English baseline. |
+| Lucene Porter source copy | Apache Lucene project | 10.5.0 source artifact | English | Package-isolated benchmark-only generated source; single output | Generated into the JMH build tree, never production code. |
+| Paice/Husk Lancaster | Upstream Java implementation from `Hopper262/paice-husk-stemmer` | Configured upstream branch/revision in `gradle/paicehusk-benchmarks.gradle`; immutable commit not recorded | English | Direct single output | Aggressive rule-based English baseline; benchmark-only generated source. |
+
+## Preprocessing and lifecycle
+
+The quality evaluator calls the same adapter matrix used by JMH. Each language mapping is explicit. Retained dictionary forms are not evaluation-lowercased or normalized. Where an implementation requires preprocessing, such as Lucene German or Persian normalization, that operation is part of its documented adapter path. Stateful TokenFilters are reset through the same sequential lifecycle used by the benchmark and are not invoked concurrently.
+
+Candidate sets are non-null, non-empty, contain the deterministic primary output, contain no null strings, and are deduplicated using exact Java string equality. Gold-standard group identity never selects, removes, or ranks a candidate.
+
+## Coverage fairness
+
+Radixor coverage is derived independently from its resources and language enumeration. Third-party coverage is the intersection of that universe with actual adapter support. Absence therefore means “not supported or not configured for this language,” not “zero quality.” Consult each language page for the exact evaluated rows.
+
+Project authors and organizations are named only where repository configuration or source notices establish attribution. No broader authorship or license claim is inferred when metadata was not captured.

docs/stemming-quality.md

@@ -0,0 +1,82 @@
+# Stemming quality evaluation
+
+The explicit `stemmingQuality` analysis measures agreement between stemmer outputs and gold-standard equivalence classes represented by bundled multilingual dictionary rows. Dictionary text remains unchanged; reports and diagnostics use English.
+
+JMH adapters, registries, third-party versions, language mappings, and preparation remain in `src/jmh`. The evaluator, reports, audits, and tests reside in the standard `src/test` source set. The former `src/stemmingQualityTest` source set was removed, and neither analytical nor JMH classes enter the production JAR.
+
+## Language and adapter coverage
+
+The authoritative Radixor universe is the validated one-to-one reconciliation of `src/main/resources/*/stemmer.gz` and every `StemmerPatchTrieLoader.Language` value. All 20 current values have exactly one resource; no sentinel or alias is excluded. Radixor is evaluated for all 20 languages, independently of third-party support. Third-party combinations come only from explicit JMH adapter metadata. Unsupported combinations are documented and never fabricated as zero-valued rows.
+
+The expected matrix is constructed before evaluation from stemmer, language, dictionary mode, and supported output policy. Generation fails on missing, duplicate, unexpected, or stale keys.
+
+## Dictionary groups and modes
+
+Every usable parsed row is one gold-standard group. Exact duplicate strings are removed only within that row; identical forms in different rows remain distinct. `ALL_WORDS` preserves every valid form. `LOWERCASE_GROUPS_ONLY` excludes a complete group containing an uppercase or titlecase Unicode code point. Retained words are not lowercased or normalized by the evaluator.
+
+## Output policies
+
+`PRIMARY_OUTPUT` uses the deterministic JMH output and defines a strict partition.
+
+For multi-output adapters, `C(w)` is the immutable, sorted, exactly deduplicated candidate set. It is non-null, non-empty, contains no null, and contains the primary output. Radixor obtains alternatives through `getAll`. The repository's Morphologik lookups can return distinct lemma strings and are multi-output. Configured Hunspell filters can emit several stems at one token position. Other adapters emit only primary rows.
+
+`ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound. A same-group pair succeeds when its sets intersect. A cross-group pair is an error only when both sets are the same singleton; otherwise unequal candidates can be selected for that pair. Choices may vary between pairs and need not form one realizable global assignment.
+
+`ALL_CANDIDATES` activates every candidate. Two forms are related when their sets intersect, for both same-group and cross-group pairs. This relation can overlap and need not be transitive. A pair sharing several candidates is counted once.
+
+The evaluator verifies:
+
+```text
+ANY under <= PRIMARY under
+ALL under <= PRIMARY under
+ANY under = ALL under
+ANY over <= PRIMARY over
+ALL over >= PRIMARY over
+```
+
+## Pair definitions and efficient counting
+
+For `C2(n) = n(n-1)/2`:
+
+```text
+underPossible = sum_g C2(n_g)
+overPossible = C2(N) - sum_g C2(n_g)
+```
+
+Under-stemming counts unrelated same-group pairs. Over-stemming counts related cross-group pairs. Primary output uses global and per-group stem frequencies. Candidate sets are canonical signatures counted globally and per group. An inverted candidate-to-signature index discovers intersections, and signature pairs shared through several candidates are deduplicated. `ANY_CANDIDATE` over-stemming uses only equal singleton signatures. All pair arithmetic uses checked `long` operations; complete production word pairs are never enumerated.
+
+## Confusion and aggregate metrics
+
+```text
+TP = underPossible - underError
+FN = underError
+FP = overError
+TN = overPossible - overError
+```
+
+Under-stemming is `FN/(TP+FN)` and over-stemming is `FP/(TN+FP)`; their denominators differ. The CSV also publishes precision, recall, specificity, accuracy, balanced accuracy, F0.5, F1, F2, Jaccard, Fowlkes-Mallows, Matthews correlation coefficient, and pairwise error rate. F0.5 emphasizes precision and over-stemming, F1 balances precision and recall, and F2 emphasizes recall and under-stemming. Accuracy and error rate can be dominated by the large cross-group true-negative population. Metrics use raw counts, not rounded rates. Zero denominators produce `n/a` in Markdown and empty CSV fields.
+
+Only `PRIMARY_OUTPUT` receives partition metrics: Adjusted Rand Index, homogeneity, completeness, V-measure, and normalized mutual information with arithmetic-mean entropy normalization. Candidate policies remain inapplicable rather than being forced into artificial partitions.
+
+Micro summaries sum confusion counts before calculation. Macro summaries average defined language values and retain coverage counts. Common-language comparisons use the exact language intersection and never score unsupported languages as zero. Rankings are separated by policy and metric; the default F0.5 choice is navigation, not a universal scientific preference.
+
+Pearson and average-tie-rank Spearman reports use unrounded values and separate dictionary-mode and output-policy cohorts. Fewer than three observations, undefined inputs, and zero variance produce documented missing values. The reports provide reproducible data and make no automatic scientific conclusion.
+
+## Exact accuracy and pairwise under-stemming
+
+Exact textual accuracy and pairwise grouping use different denominators. One erroneous form in a 12-form group creates 11 erroneous pairs: with 88 singleton groups, exact accuracy can be 99% while pairwise under-stemming is `11/C2(12) = 16.666667%`. Singleton groups affect word accuracy but add no within-group pairs.
+
+## Running the analysis
+
+```bash
+./gradlew stemmingQuality
+./gradlew stemmingQuality -PstemmingQualityStemmer=Radixor -PstemmingQualityLanguage=DE_DE -PstemmingQualityMode=ALL_WORDS -PstemmingQualityAudit=true
+```
+
+Optional properties are `stemmingQualityLanguage`, `stemmingQualityStemmer`, `stemmingQualityMode`, `stemmingQualityOutputPolicy`, `stemmingQualityRankMetric`, `stemmingQualityAudit`, and `stemmingQualityAuditLimit`. Policies are `PRIMARY_OUTPUT`, `ANY_CANDIDATE`, and `ALL_CANDIDATES`. Filtered reports carry `-filtered` and cannot overwrite complete output.
+
+Generated files under `build/reports/stemming-quality/` include `stemming-quality.md`, `stemming-quality.csv`, `metric-correlations-pearson.csv`, `metric-correlations-spearman.csv`, and optional audit Markdown.
+
+## Limitations
+
+These measurements evaluate agreement with the available dictionary grouping. They do not capture every semantic, morphological, downstream, or dataset-specific property. `ANY_CANDIDATE` is optimistic and may not be globally realizable. `ALL_CANDIDATES` measures an overlap graph rather than a partition. Language coverage must remain visible in cross-stemmer comparisons. No single published metric establishes universal superiority; multiple metrics and their correlations are provided for transparent scientific assessment.

gradle.lockfile

@@ -5,15 +5,15 @@ antlr:antlr:2.7.7=pitest
 com.github.oowekyala.ooxml:nice-xml-messages:3.1=pmd
 com.google.code.gson:gson:2.13.2=pmd
 com.google.errorprone:error_prone_annotations:2.41.0=pmd
-net.bytebuddy:byte-buddy-agent:1.17.7=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+net.bytebuddy:byte-buddy-agent:1.17.7=testCompileClasspath,testRuntimeClasspath
-net.bytebuddy:byte-buddy:1.17.7=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+net.bytebuddy:byte-buddy:1.17.7=testCompileClasspath,testRuntimeClasspath
-net.jqwik:jqwik-api:1.9.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+net.jqwik:jqwik-api:1.9.3=testCompileClasspath,testRuntimeClasspath
-net.jqwik:jqwik-engine:1.9.3=jmhRuntimeClasspath,testRuntimeClasspath
+net.jqwik:jqwik-engine:1.9.3=testRuntimeClasspath
-net.jqwik:jqwik-time:1.9.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+net.jqwik:jqwik-time:1.9.3=testCompileClasspath,testRuntimeClasspath
-net.jqwik:jqwik-web:1.9.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+net.jqwik:jqwik-web:1.9.3=testCompileClasspath,testRuntimeClasspath
-net.jqwik:jqwik:1.9.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+net.jqwik:jqwik:1.9.3=testCompileClasspath,testRuntimeClasspath
 net.sf.jopt-simple:jopt-simple:4.9=pitest
-net.sf.jopt-simple:jopt-simple:5.0.4=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+net.sf.jopt-simple:jopt-simple:5.0.4=jmh,jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
 net.sf.saxon:Saxon-HE:12.9=pmd
 net.sourceforge.pmd:pmd-ant:7.20.0=pmd
 net.sourceforge.pmd:pmd-core:7.20.0=pmd
@@ -22,45 +22,45 @@ org.antlr:antlr4-runtime:4.9.3=pmd
 org.antlr:stringtemplate:3.2.1=pitest
 org.apache.commons:commons-lang3:3.18.0=pitest
 org.apache.commons:commons-lang3:3.20.0=pmd
-org.apache.commons:commons-math3:3.6.1=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.apache.commons:commons-math3:3.6.1=jmh,jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
 org.apache.commons:commons-text:1.14.0=pitest
-org.apache.lucene:lucene-analysis-common:10.5.0=jmhCompileClasspath,jmhRuntimeClasspath
+org.apache.lucene:lucene-analysis-common:10.5.0=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
-org.apache.lucene:lucene-analysis-morfologik:10.5.0=jmhCompileClasspath,jmhRuntimeClasspath
+org.apache.lucene:lucene-analysis-morfologik:10.5.0=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
-org.apache.lucene:lucene-analysis-stempel:10.5.0=jmhCompileClasspath,jmhRuntimeClasspath
+org.apache.lucene:lucene-analysis-stempel:10.5.0=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
-org.apache.lucene:lucene-core:10.5.0=jmhCompileClasspath,jmhRuntimeClasspath
+org.apache.lucene:lucene-core:10.5.0=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
-org.apache.opennlp:opennlp-tools:2.5.4=jmhCompileClasspath,jmhRuntimeClasspath
+org.apache.opennlp:opennlp-tools:2.5.4=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
-org.apiguardian:apiguardian-api:1.1.2=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.apiguardian:apiguardian-api:1.1.2=testCompileClasspath,testRuntimeClasspath
-org.carrot2:morfologik-fsa:2.1.9=jmhCompileClasspath,jmhRuntimeClasspath
+org.carrot2:morfologik-fsa:2.1.9=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
-org.carrot2:morfologik-polish:2.1.9=jmhRuntimeClasspath
+org.carrot2:morfologik-polish:2.1.9=jmhRuntimeClasspath,stemmingQualityJmhRuntime
-org.carrot2:morfologik-stemming:2.1.9=jmhCompileClasspath,jmhRuntimeClasspath
+org.carrot2:morfologik-stemming:2.1.9=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
 org.checkerframework:checker-qual:3.52.1=pmd
 org.jacoco:org.jacoco.agent:0.8.14=jacocoAgent,jacocoAnt
 org.jacoco:org.jacoco.ant:0.8.14=jacocoAnt
 org.jacoco:org.jacoco.core:0.8.14=jacocoAnt
 org.jacoco:org.jacoco.report:0.8.14=jacocoAnt
-org.junit.jupiter:junit-jupiter-api:5.14.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.junit.jupiter:junit-jupiter-api:5.14.3=testCompileClasspath,testRuntimeClasspath
-org.junit.jupiter:junit-jupiter-engine:5.14.3=jmhRuntimeClasspath,testRuntimeClasspath
+org.junit.jupiter:junit-jupiter-engine:5.14.3=testRuntimeClasspath
-org.junit.jupiter:junit-jupiter-params:5.14.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.junit.jupiter:junit-jupiter-params:5.14.3=testCompileClasspath,testRuntimeClasspath
-org.junit.jupiter:junit-jupiter:5.14.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.junit.jupiter:junit-jupiter:5.14.3=testCompileClasspath,testRuntimeClasspath
-org.junit.platform:junit-platform-commons:1.14.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.junit.platform:junit-platform-commons:1.14.3=testCompileClasspath,testRuntimeClasspath
-org.junit.platform:junit-platform-engine:1.14.3=jmhRuntimeClasspath,testRuntimeClasspath
+org.junit.platform:junit-platform-engine:1.14.3=testRuntimeClasspath
-org.junit.platform:junit-platform-launcher:1.14.3=jmhRuntimeClasspath,testRuntimeClasspath
+org.junit.platform:junit-platform-launcher:1.14.3=testRuntimeClasspath
-org.junit:junit-bom:5.14.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.junit:junit-bom:5.14.3=testCompileClasspath,testRuntimeClasspath
-org.mockito:mockito-core:5.23.0=jmhRuntimeClasspath,mockitoAgent,testCompileClasspath,testRuntimeClasspath
+org.mockito:mockito-core:5.23.0=mockitoAgent,testCompileClasspath,testRuntimeClasspath
-org.mockito:mockito-junit-jupiter:5.23.0=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.mockito:mockito-junit-jupiter:5.23.0=testCompileClasspath,testRuntimeClasspath
-org.objenesis:objenesis:3.3=jmhRuntimeClasspath,testRuntimeClasspath
+org.objenesis:objenesis:3.3=testRuntimeClasspath
-org.openjdk.jmh:jmh-core:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.openjdk.jmh:jmh-core:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
-org.openjdk.jmh:jmh-generator-asm:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.openjdk.jmh:jmh-generator-asm:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
-org.openjdk.jmh:jmh-generator-bytecode:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.openjdk.jmh:jmh-generator-bytecode:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
-org.openjdk.jmh:jmh-generator-reflection:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.openjdk.jmh:jmh-generator-reflection:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
-org.opentest4j:opentest4j:1.3.0=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
+org.opentest4j:opentest4j:1.3.0=testCompileClasspath,testRuntimeClasspath
 org.ow2.asm:asm-analysis:9.9.1=pitest
 org.ow2.asm:asm-commons:9.9=jacocoAnt
 org.ow2.asm:asm-commons:9.9.1=pitest
 org.ow2.asm:asm-tree:9.9=jacocoAnt
 org.ow2.asm:asm-tree:9.9.1=pitest
 org.ow2.asm:asm-util:9.9.1=pitest
-org.ow2.asm:asm:9.0=jmh,jmhCompileClasspath,jmhRuntimeClasspath
+org.ow2.asm:asm:9.0=jmh,jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
 org.ow2.asm:asm:9.9=jacocoAnt
 org.ow2.asm:asm:9.9.1=pitest,pmd
 org.pcollections:pcollections:4.0.2=pmd
@@ -70,7 +70,7 @@ org.pitest:pitest-html-report:1.22.1=pitest
 org.pitest:pitest-junit5-plugin:1.2.3=pitest
 org.pitest:pitest:1.22.1=pitest
 org.slf4j:jul-to-slf4j:1.7.36=pmd
-org.slf4j:slf4j-api:2.0.17=jmhCompileClasspath,jmhRuntimeClasspath
+org.slf4j:slf4j-api:2.0.17=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
 org.xmlresolver:xmlresolver:5.3.3=pmd
-ua.net.nlp:morfologik-ukrainian-search:4.9.1=jmhRuntimeClasspath
+ua.net.nlp:morfologik-ukrainian-search:4.9.1=jmhRuntimeClasspath,stemmingQualityJmhRuntime
 empty=annotationProcessor,compileClasspath,cyclonedxBom,jmhAnnotationProcessor,mainPmdAuxClasspath,runtimeClasspath,testAnnotationProcessor

gradle/cistem-benchmarks.gradle

@@ -0,0 +1,69 @@
+def cistemGoldStandardBaseUrl = 'https://raw.githubusercontent.com/LeonieWeissweiler/CISTEM/refs/heads/master/gold_standards'
+def cistemGoldStandardFiles = [
+        'goldstandard1.txt',
+        'goldstandard2.txt'
+]
+def cistemGoldStandardDownloadDirectory = layout.buildDirectory.dir('third-party/cistem-gold-standards')
+def cistemGoldStandardGeneratedResourcesDirectory = layout.buildDirectory.dir('generated/resources/cistem-gold-standards')
+
+def cistemGoldStandardDownloadedFiles = cistemGoldStandardFiles.collect { String fileName ->
+    cistemGoldStandardDownloadDirectory.map { it.file(fileName) }
+}
+
+tasks.register('downloadCistemGoldStandards') {
+    group = 'build setup'
+    description = 'Downloads benchmark-only CISTEM German gold standards.'
+
+    outputs.files(cistemGoldStandardDownloadedFiles)
+
+    doLast {
+        cistemGoldStandardFiles.each { String fileName ->
+            final File targetFile = cistemGoldStandardDownloadDirectory.get().file(fileName).asFile
+            targetFile.parentFile.mkdirs()
+
+            if (!targetFile.exists()) {
+                final URL sourceUrl = new URL("${cistemGoldStandardBaseUrl}/${fileName}")
+                try {
+                    sourceUrl.withInputStream { inputStream ->
+                        targetFile.withOutputStream { outputStream ->
+                            outputStream << inputStream
+                        }
+                    }
+                } catch (FileNotFoundException exception) {
+                    throw new GradleException(
+                            "Unable to download CISTEM gold standard ${fileName} from ${sourceUrl}.",
+                            exception)
+                }
+            }
+
+            if (targetFile.length() <= 0L) {
+                throw new GradleException("Downloaded CISTEM gold standard ${fileName} was empty.")
+            }
+        }
+    }
+}
+
+tasks.register('prepareCistemGoldStandardResources', Copy) {
+    group = 'build setup'
+    description = 'Copies benchmark-only CISTEM German gold standards into the JMH resource output.'
+
+    dependsOn(tasks.named('downloadCistemGoldStandards'))
+
+    from(cistemGoldStandardDownloadDirectory) {
+        include 'goldstandard1.txt'
+        include 'goldstandard2.txt'
+    }
+    into(cistemGoldStandardGeneratedResourcesDirectory)
+}
+
+sourceSets {
+    jmh {
+        resources {
+            srcDir(cistemGoldStandardGeneratedResourcesDirectory)
+        }
+    }
+}
+
+tasks.named('processJmhResources') {
+    dependsOn(tasks.named('prepareCistemGoldStandardResources'))
+}

gradle/hunspell-benchmarks.gradle

@@ -0,0 +1,121 @@
+import org.gradle.plugins.ide.eclipse.model.SourceFolder
+
+
+def hunspellDictionaryBaseUrl = 'https://raw.githubusercontent.com/wooorm/dictionaries/main/dictionaries'
+def hunspellDictionaryLanguages = [
+        en: 'English',
+        cs: 'Czech',
+        de: 'German',
+        es: 'Spanish',
+        fr: 'French',
+        nl: 'Dutch',
+        pl: 'Polish',
+        uk: 'Ukrainian'
+]
+def hunspellDownloadDirectory = layout.buildDirectory.dir('third-party/hunspell')
+def hunspellGeneratedResourcesDirectory = layout.buildDirectory.dir('generated/resources/hunspell')
+def hunspellGeneratedResourcesPath = provider {
+    project.relativePath(hunspellGeneratedResourcesDirectory.get().asFile)
+}
+def hunspellEclipseClasspathAttributes = [
+        gradle_scope        : 'jmh',
+        gradle_used_by_scope: 'jmh',
+        test                : 'true'
+]
+def hunspellIsAbsolutePath = { String path ->
+    path.startsWith('/') || path ==~ /^[A-Za-z]:[\\\/].*/
+}
+
+def hunspellDownloadedFiles = hunspellDictionaryLanguages.keySet().collectMany { String code ->
+    [
+            hunspellDownloadDirectory.map { it.file("${code}/index.aff") },
+            hunspellDownloadDirectory.map { it.file("${code}/index.dic") },
+            hunspellDownloadDirectory.map { it.file("${code}/license") }
+    ]
+}
+
+tasks.register('downloadHunspellBenchmarkDictionaries') {
+    group = 'build setup'
+    description = 'Downloads benchmark-only Hunspell dictionaries from wooorm/dictionaries.'
+
+    outputs.files(hunspellDownloadedFiles)
+
+    doLast {
+        hunspellDictionaryLanguages.each { String code, String displayName ->
+            ['index.aff', 'index.dic', 'license'].each { String fileName ->
+                final File targetFile = hunspellDownloadDirectory.get().file("${code}/${fileName}").asFile
+                targetFile.parentFile.mkdirs()
+
+                if (!targetFile.exists()) {
+                    final URL sourceUrl = new URL("${hunspellDictionaryBaseUrl}/${code}/${fileName}")
+                    try {
+                        sourceUrl.withInputStream { inputStream ->
+                            targetFile.withOutputStream { outputStream ->
+                                outputStream << inputStream
+                            }
+                        }
+                    } catch (FileNotFoundException exception) {
+                        throw new GradleException(
+                                "Unable to download Hunspell ${fileName} file for ${displayName} (${code}) from ${sourceUrl}.",
+                                exception)
+                    }
+                }
+
+                if (targetFile.length() <= 0L) {
+                    throw new GradleException("Downloaded Hunspell ${fileName} file for ${displayName} was empty.")
+                }
+            }
+        }
+    }
+}
+
+tasks.register('prepareHunspellBenchmarkResources', Copy) {
+    group = 'build setup'
+    description = 'Copies benchmark-only Hunspell dictionaries into the JMH resource output.'
+
+    dependsOn(tasks.named('downloadHunspellBenchmarkDictionaries'))
+
+    from(hunspellDownloadDirectory) {
+        include '**/index.aff'
+        include '**/index.dic'
+        include '**/license'
+        into 'hunspell'
+    }
+    into(hunspellGeneratedResourcesDirectory)
+}
+
+sourceSets {
+    jmh {
+        resources {
+            srcDir(hunspellGeneratedResourcesDirectory)
+        }
+    }
+}
+
+tasks.named('processJmhResources') {
+    dependsOn(tasks.named('prepareHunspellBenchmarkResources'))
+}
+
+eclipse {
+    classpath {
+        file {
+            whenMerged { classpath ->
+                String generatedPath = hunspellGeneratedResourcesPath.get()
+
+                classpath.entries.removeAll { entry ->
+                    entry.hasProperty('path') && (
+                            entry.path == generatedPath ||
+                            hunspellIsAbsolutePath(entry.path)
+                    )
+                }
+
+                SourceFolder hunspellEntry = new SourceFolder(generatedPath, null)
+                hunspellEntry.output = 'bin/jmh'
+                hunspellEclipseClasspathAttributes.each { String name, String value ->
+                    hunspellEntry.entryAttributes[name] = value
+                }
+                classpath.entries.add(hunspellEntry)
+            }
+        }
+    }
+}

mkdocs.yml

@@ -68,6 +68,9 @@ nav:
      - Benchmark Results: benchmarks/index.md
      - Reference:
         - Methodology: benchmarks/reference/methodology.md
+        - Linguistic Quality Methodology: benchmarks/reference/linguistic-quality.md
+        - Tested Stemmers: benchmarks/reference/tested-stemmers.md
+        - Reproducibility and Raw Data: benchmarks/reference/reproducibility.md
         - Corpora: benchmarks/reference/corpora.md
         - Environment and Reports: benchmarks/reference/environment.md
         - English Dictionary Coverage: benchmarks/reference/english-coverage.md
@@ -96,5 +99,6 @@ nav:
 
   - Quality and Operations:
      - Quality and Operations: quality-and-operations.md
+     - Stemming Quality: stemming-quality.md
      - Reports: reports.md
      - Test taxonomy and execution filtering: test-taxonomy-and-filtering.md

src/jmh/java/org/egothor/stemmer/benchmark/Cistem.java

@@ -0,0 +1,178 @@
+/*******************************************************************************
+ * MIT License
+ *
+ * Copyright (c) 2017 Leonie Weißweiler
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ *
+ * Source: CISTEM German stemmer
+ *   Authors: Leonie Weissweiler, Alexander Fraser
+ *   https://github.com/LeonieWeissweiler/CISTEM
+ *   https://www.cis.lmu.de/~weissweiler/cistem/
+ ******************************************************************************/
+package org.egothor.stemmer.benchmark;
+
+import java.util.regex.Pattern;
+
+public final class Cistem {
+
+    private static final Pattern GE_PATTERN = Pattern.compile("^ge(.{4,})");
+    private static final Pattern DOLLAR1_PATTERN = Pattern.compile("(.)\\1");
+    private static final Pattern ND_PATTERN = Pattern.compile("nd$");
+    private static final Pattern EMR_PATTERN = Pattern.compile("e[mr]$");
+    private static final Pattern T_PATTERN = Pattern.compile("t$");
+    private static final Pattern ESN_PATTERN = Pattern.compile("[esn]$");
+    private static final Pattern STAR_PATTERN = Pattern.compile("(.)\\*");
+
+    private Cistem() {
+    }
+
+    public static String stem(final String word) {
+        return stem(word, false);
+    }
+
+    public static String stem(final String word, final boolean caseInsensitive) {
+        if (word.isEmpty()) {
+            return word;
+        }
+
+        String normalized = word;
+        normalized = normalized.replace("Ü", "U");
+        normalized = normalized.replace("Ö", "O");
+        normalized = normalized.replace("Ä", "A");
+        normalized = normalized.replace("ü", "u");
+        normalized = normalized.replace("ö", "o");
+        normalized = normalized.replace("ä", "a");
+
+        final boolean uppercase = Character.isUpperCase(normalized.charAt(0));
+
+        normalized = normalized.toLowerCase();
+        normalized = normalized.replace("ß", "ss");
+        normalized = GE_PATTERN.matcher(normalized).replaceAll("$1");
+        normalized = normalized.replace("sch", "$");
+        normalized = normalized.replace("ei", "%");
+        normalized = normalized.replace("ie", "&");
+        normalized = DOLLAR1_PATTERN.matcher(normalized).replaceAll("$1*");
+
+        while (normalized.length() > 3) {
+            if (normalized.length() > 5) {
+                String newWord = EMR_PATTERN.matcher(normalized).replaceAll("");
+                if (!normalized.equals(newWord)) {
+                    normalized = newWord;
+                    continue;
+                }
+
+                newWord = ND_PATTERN.matcher(normalized).replaceAll("");
+                if (!normalized.equals(newWord)) {
+                    normalized = newWord;
+                    continue;
+                }
+            }
+
+            if (!uppercase || caseInsensitive) {
+                final String newWord = T_PATTERN.matcher(normalized).replaceAll("");
+                if (!normalized.equals(newWord)) {
+                    normalized = newWord;
+                    continue;
+                }
+            }
+
+            final String newWord = ESN_PATTERN.matcher(normalized).replaceAll("");
+            if (!normalized.equals(newWord)) {
+                normalized = newWord;
+            } else {
+                break;
+            }
+        }
+
+        normalized = STAR_PATTERN.matcher(normalized).replaceAll("$1$1");
+        normalized = normalized.replace("&", "ie");
+        normalized = normalized.replace("%", "ei");
+        normalized = normalized.replace("$", "sch");
+
+        return normalized;
+    }
+
+    public static String[] segment(final String word) {
+        return segment(word, false);
+    }
+
+    public static String[] segment(final String word, final boolean caseInsensitive) {
+        if (word.isEmpty()) {
+            return new String[] {"", ""};
+        }
+
+        int restLength = 0;
+        final boolean uppercase = Character.isUpperCase(word.charAt(0));
+        String normalized = word.toLowerCase();
+        final String original = new String(normalized);
+
+        normalized = normalized.replace("sch", "$");
+        normalized = normalized.replace("ei", "%");
+        normalized = normalized.replace("ie", "&");
+        normalized = DOLLAR1_PATTERN.matcher(normalized).replaceAll("$1*");
+
+        while (normalized.length() > 3) {
+            if (normalized.length() > 5) {
+                String newWord = normalized.replaceAll("e[mr]$", "");
+                if (!normalized.equals(newWord)) {
+                    restLength += 2;
+                    normalized = newWord;
+                    continue;
+                }
+
+                newWord = normalized.replaceAll("nd$", "");
+                if (!normalized.equals(newWord)) {
+                    restLength += 2;
+                    normalized = newWord;
+                    continue;
+                }
+            }
+
+            if (!uppercase || caseInsensitive) {
+                final String newWord = normalized.replaceAll("t$", "");
+                if (!normalized.equals(newWord)) {
+                    restLength += 1;
+                    normalized = newWord;
+                    continue;
+                }
+            }
+
+            final String newWord = normalized.replaceAll("[esn]$", "");
+            if (!normalized.equals(newWord)) {
+                restLength += 1;
+                normalized = newWord;
+            } else {
+                break;
+            }
+        }
+
+        normalized = normalized.replaceAll("(.)\\*", "$1$1");
+        normalized = normalized.replace("&", "ie");
+        normalized = normalized.replace("%", "ei");
+        normalized = normalized.replace("$", "sch");
+
+        String rest = "";
+        if (restLength != 0) {
+            rest = original.substring(original.length() - restLength);
+        }
+
+        return new String[] {normalized, rest};
+    }
+}

src/jmh/java/org/egothor/stemmer/benchmark/EnglishHunspellStemmerComparisonBenchmarkQuality.java

@@ -0,0 +1,280 @@
+/*******************************************************************************
+ * Copyright (C) 2026, Leo Galambos
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice,
+ *    this list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ *    this list of conditions and the following disclaimer in the documentation
+ *    and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its contributors
+ *    may be used to endorse or promote products derived from this software
+ *    without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+ * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+ * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+ * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+ * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+ * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+ * POSSIBILITY OF SUCH DAMAGE.
+ ******************************************************************************/
+package org.egothor.stemmer.benchmark;
+
+import java.io.IOException;
+import java.io.InputStream;
+import java.text.ParseException;
+import java.util.List;
+import java.util.Objects;
+import java.util.concurrent.TimeUnit;
+
+import org.apache.lucene.analysis.LowerCaseFilter;
+import org.apache.lucene.analysis.TokenStream;
+import org.apache.lucene.analysis.hunspell.Dictionary;
+import org.apache.lucene.analysis.hunspell.HunspellStemFilter;
+import org.apache.lucene.analysis.hunspell.SortingStrategy;
+import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
+import org.apache.lucene.analysis.tokenattributes.PositionIncrementAttribute;
+import org.egothor.stemmer.StemmerPatchTrieLoader;
+import org.openjdk.jmh.annotations.AuxCounters;
+import org.openjdk.jmh.annotations.Benchmark;
+import org.openjdk.jmh.annotations.BenchmarkMode;
+import org.openjdk.jmh.annotations.Fork;
+import org.openjdk.jmh.annotations.Level;
+import org.openjdk.jmh.annotations.Measurement;
+import org.openjdk.jmh.annotations.Mode;
+import org.openjdk.jmh.annotations.OutputTimeUnit;
+import org.openjdk.jmh.annotations.Scope;
+import org.openjdk.jmh.annotations.Setup;
+import org.openjdk.jmh.annotations.State;
+import org.openjdk.jmh.annotations.Warmup;
+import org.openjdk.jmh.infra.Blackhole;
+
+/**
+ * Emits exact-root agreement metrics for the benchmark-only English Hunspell
+ * comparison.
+ */
+@BenchmarkMode(Mode.AverageTime)
+@OutputTimeUnit(TimeUnit.NANOSECONDS)
+@Warmup(iterations = 0)
+@Measurement(iterations = 1, time = 1, timeUnit = TimeUnit.MILLISECONDS)
+@Fork(0)
+public class EnglishHunspellStemmerComparisonBenchmarkQuality {
+
+    /**
+     * Shared English quality corpus and Hunspell dictionary.
+     */
+    @State(Scope.Benchmark)
+    public static class SharedState {
+
+        /**
+         * Complete English resource-derived corpus.
+         */
+        private LanguageBenchmarkCorpus.Corpus corpus;
+
+        /**
+         * Parsed benchmark-only Hunspell dictionary.
+         */
+        private Dictionary dictionary;
+
+        /**
+         * Initializes quality resources.
+         *
+         * @throws IOException    if corpus or dictionary loading fails
+         * @throws ParseException if the Hunspell dictionary cannot be parsed
+         */
+        @Setup(Level.Trial)
+        public void setUp() throws IOException, ParseException {
+            this.corpus = LanguageBenchmarkCorpus.createFullCorpus(StemmerPatchTrieLoader.Language.US_UK);
+            this.dictionary = loadEnglishDictionary();
+        }
+    }
+
+    /**
+     * JMH auxiliary counters for exact-root agreement.
+     */
+    @State(Scope.Thread)
+    @AuxCounters(AuxCounters.Type.EVENTS)
+    public static class AccuracyCounters {
+
+        /**
+         * Number of exact-root matches.
+         */
+        public long correctMatches;
+
+        /**
+         * Number of evaluated tokens.
+         */
+        public long evaluatedTokens;
+
+        /**
+         * Number of exact-root matches where the input token differs from the
+         * expected root.
+         */
+        public long changedCorrectMatches;
+
+        /**
+         * Number of evaluated tokens where the input token differs from the expected
+         * root.
+         */
+        public long changedEvaluatedTokens;
+
+        /**
+         * Number of exact-root matches where the input token is already the expected
+         * root.
+         */
+        public long rootPreservedMatches;
+
+        /**
+         * Number of evaluated tokens where the input token is already the expected
+         * root.
+         */
+        public long rootEvaluatedTokens;
+
+        /**
+         * Resets counters before the measured iteration.
+         */
+        @Setup(Level.Iteration)
+        public void reset() {
+            this.correctMatches = 0L;
+            this.evaluatedTokens = 0L;
+            this.changedCorrectMatches = 0L;
+            this.changedEvaluatedTokens = 0L;
+            this.rootPreservedMatches = 0L;
+            this.rootEvaluatedTokens = 0L;
+        }
+    }
+
+    /**
+     * Evaluates exact-root agreement for English Hunspell.
+     *
+     * @param sharedState shared English quality state
+     * @param counters    JMH auxiliary counters
+     * @param blackhole   result sink
+     * @return exact-root match count
+     * @throws IOException if Lucene token streaming fails
+     */
+    @Benchmark
+    public int luceneHunspellStemFilterAccuracy(final SharedState sharedState, final AccuracyCounters counters,
+            final Blackhole blackhole) throws IOException {
+        final String[] actualStems = firstHunspellOutputs(sharedState.corpus.tokens(), sharedState.dictionary,
+                blackhole);
+        final String[] tokens = sharedState.corpus.tokens();
+        final String[] expectedRoots = sharedState.corpus.expectedRoots();
+
+        int correct = 0;
+        int changedCorrect = 0;
+        int changedEvaluated = 0;
+        int rootPreserved = 0;
+        int rootEvaluated = 0;
+        for (int index = 0; index < actualStems.length; index++) {
+            final String token = tokens[index];
+            final String expectedRoot = expectedRoots[index];
+            final boolean exact = Objects.equals(expectedRoot, actualStems[index]);
+            if (exact) {
+                correct++;
+            }
+            if (Objects.equals(token, expectedRoot)) {
+                rootEvaluated++;
+                if (exact) {
+                    rootPreserved++;
+                }
+            } else {
+                changedEvaluated++;
+                if (exact) {
+                    changedCorrect++;
+                }
+            }
+        }
+
+        counters.correctMatches += correct;
+        counters.evaluatedTokens += actualStems.length;
+        counters.changedCorrectMatches += changedCorrect;
+        counters.changedEvaluatedTokens += changedEvaluated;
+        counters.rootPreservedMatches += rootPreserved;
+        counters.rootEvaluatedTokens += rootEvaluated;
+        return correct;
+    }
+
+    /**
+     * Extracts the first emitted Hunspell stem for each input token.
+     *
+     * @param tokens     token corpus
+     * @param dictionary Hunspell dictionary
+     * @param blackhole  result sink
+     * @return first emitted term per input token
+     * @throws IOException if Lucene streaming fails
+     */
+    private static String[] firstHunspellOutputs(final String[] tokens, final Dictionary dictionary,
+            final Blackhole blackhole) throws IOException {
+        final String[] outputs = new String[tokens.length];
+        final BenchmarkTokenStream input = new BenchmarkTokenStream(tokens);
+        final TokenStream output = new HunspellStemFilter(new LowerCaseFilter(input), dictionary, true);
+        final CharTermAttribute termAttribute = output.addAttribute(CharTermAttribute.class);
+        final PositionIncrementAttribute positionAttribute = output.addAttribute(PositionIncrementAttribute.class);
+        int inputIndex = -1;
+        boolean recordedForPosition = false;
+
+        output.reset();
+        while (output.incrementToken()) {
+            final int positionIncrement = positionAttribute.getPositionIncrement();
+            if (positionIncrement > 0) {
+                inputIndex += positionIncrement;
+                recordedForPosition = false;
+            }
+            if (inputIndex >= 0 && inputIndex < outputs.length && !recordedForPosition) {
+                outputs[inputIndex] = termAttribute.toString();
+                recordedForPosition = true;
+            }
+            blackhole.consume(termAttribute);
+        }
+        output.end();
+        output.close();
+
+        for (int index = 0; index < outputs.length; index++) {
+            if (outputs[index] == null) {
+                outputs[index] = tokens[index];
+            }
+        }
+        return outputs;
+    }
+
+    /**
+     * Loads the benchmark-only English Hunspell dictionary.
+     *
+     * @return parsed Hunspell dictionary
+     * @throws IOException    if dictionary resources cannot be read
+     * @throws ParseException if the Hunspell dictionary cannot be parsed
+     */
+    private static Dictionary loadEnglishDictionary() throws IOException, ParseException {
+        final ClassLoader classLoader = EnglishHunspellStemmerComparisonBenchmarkQuality.class.getClassLoader();
+        try (InputStream affixStream = openRequiredResource(classLoader, "hunspell/en/index.aff");
+                InputStream dictionaryStream = openRequiredResource(classLoader, "hunspell/en/index.dic")) {
+            return new Dictionary(affixStream, List.of(dictionaryStream), true, SortingStrategy.inMemory());
+        }
+    }
+
+    /**
+     * Opens a required classpath resource.
+     *
+     * @param classLoader class loader
+     * @param path        resource path
+     * @return resource stream
+     */
+    private static InputStream openRequiredResource(final ClassLoader classLoader, final String path) {
+        final InputStream stream = classLoader.getResourceAsStream(path);
+        if (stream == null) {
+            throw new IllegalStateException("Missing benchmark-only Hunspell resource: " + path);
+        }
+        return stream;
+    }
+}

src/jmh/java/org/egothor/stemmer/benchmark/GermanGoldstandardStemmerComparisonBenchmark.java

@@ -0,0 +1,579 @@
+/*******************************************************************************
+ * Copyright (C) 2026, Leo Galambos
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice,
+ *    this list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ *    this list of conditions and the following disclaimer in the documentation
+ *    and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its contributors
+ *    may be used to endorse or promote products derived from this software
+ *    without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+ * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+ * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+ * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+ * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+ * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+ * POSSIBILITY OF SUCH DAMAGE.
+ ******************************************************************************/
+package org.egothor.stemmer.benchmark;
+
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
+import java.nio.charset.StandardCharsets;
+import java.util.ArrayList;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Objects;
+import java.util.function.Function;
+
+import org.apache.lucene.analysis.LowerCaseFilter;
+import org.apache.lucene.analysis.TokenStream;
+import org.apache.lucene.analysis.de.GermanLightStemFilter;
+import org.apache.lucene.analysis.de.GermanMinimalStemFilter;
+import org.apache.lucene.analysis.de.GermanNormalizationFilter;
+import org.apache.lucene.analysis.de.GermanStemFilter;
+import org.apache.lucene.analysis.snowball.SnowballFilter;
+import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
+import org.apache.lucene.analysis.tokenattributes.PositionIncrementAttribute;
+import org.openjdk.jmh.annotations.AuxCounters;
+import org.openjdk.jmh.annotations.Benchmark;
+import org.openjdk.jmh.annotations.BenchmarkMode;
+import org.openjdk.jmh.annotations.Fork;
+import org.openjdk.jmh.annotations.Level;
+import org.openjdk.jmh.annotations.Measurement;
+import org.openjdk.jmh.annotations.Mode;
+import org.openjdk.jmh.annotations.OutputTimeUnit;
+import org.openjdk.jmh.annotations.Param;
+import org.openjdk.jmh.annotations.Scope;
+import org.openjdk.jmh.annotations.Setup;
+import org.openjdk.jmh.annotations.State;
+import org.openjdk.jmh.annotations.Warmup;
+import org.openjdk.jmh.infra.Blackhole;
+
+import org.egothor.stemmer.FrequencyTrie;
+import org.egothor.stemmer.ReductionMode;
+import org.egothor.stemmer.StemmerPatchTrieLoader;
+
+/**
+ * German-only stemmer comparison on CISTEM gold standards.
+ *
+ * <p>
+ * Each benchmark operation is fed by one cluster file. The same candidate set is
+ * evaluated twice, once per file, to produce one precision/recall/f-measure
+ * table for each gold standard.
+ * </p>
+ */
+@BenchmarkMode(Mode.AverageTime)
+@OutputTimeUnit(java.util.concurrent.TimeUnit.NANOSECONDS)
+@Warmup(iterations = 3, time = 1, timeUnit = java.util.concurrent.TimeUnit.SECONDS)
+@Measurement(iterations = 5, time = 1, timeUnit = java.util.concurrent.TimeUnit.SECONDS)
+@Fork(1)
+public class GermanGoldstandardStemmerComparisonBenchmark {
+
+    /**
+     * Shared German benchmark state for one dataset and one candidate.
+     */
+    @State(Scope.Benchmark)
+    public static class SharedState {
+
+        /**
+         * Gold standard dataset.
+         */
+        @Param({"goldstandard1.txt", "goldstandard2.txt"})
+        public String goldStandardFileName;
+
+        /**
+         * Candidate stemmer.
+         */
+        @Param({
+                "GERMAN_RADIXOR",
+                "GERMAN_LUCENE_GERMAN_STEM_FILTER",
+                "GERMAN_LUCENE_GERMAN_LIGHT_STEM_FILTER",
+                "GERMAN_LUCENE_GERMAN_MINIMAL_STEM_FILTER",
+                "GERMAN_CISTEM",
+                "SNOWBALL_GERMAN_DIRECT",
+                "SNOWBALL_GERMAN_LUCENE_FILTER"
+        })
+        public String candidateName;
+
+        /**
+         * Parsed gold standard corpus.
+         */
+        private GermanGoldstandardCorpus corpus;
+
+        /**
+         * Gold standard words flattened by cluster order.
+         */
+        private String[] allTokens;
+
+        /**
+         * Candidate evaluator.
+         */
+        private GoldstandardStemmer stemmer;
+
+        /**
+         * Initializes one candidate on one gold standard corpus.
+         *
+         * @throws IOException when the corpus cannot be loaded
+         */
+        @Setup(Level.Trial)
+        public void setUp() throws IOException {
+            this.corpus = loadCorpus(this.goldStandardFileName);
+            this.allTokens = flattenCorpusTokens(this.corpus);
+            this.stemmer = GermanCandidate.valueOf(this.candidateName).createEvaluator();
+        }
+    }
+
+    /**
+     * JMH auxiliary counters for CISTEM-style cluster accounting.
+     */
+    @State(Scope.Thread)
+    @AuxCounters(AuxCounters.Type.EVENTS)
+    public static class GoldstandardQualityCounters {
+
+        /**
+         * True positives across clusters.
+         */
+        public long truePositives;
+
+        /**
+         * False positives across clusters.
+         */
+        public long falsePositives;
+
+        /**
+         * False negatives across clusters.
+         */
+        public long falseNegatives;
+
+        /**
+         * Evaluated clusters.
+         */
+        public long evaluatedClusters;
+
+        /**
+         * Evaluated tokens.
+         */
+        public long evaluatedTokens;
+
+        /**
+         * Resets counters before each measured iteration.
+         */
+        @Setup(Level.Iteration)
+        public void reset() {
+            this.truePositives = 0L;
+            this.falsePositives = 0L;
+            this.falseNegatives = 0L;
+            this.evaluatedClusters = 0L;
+            this.evaluatedTokens = 0L;
+        }
+    }
+
+    /**
+     * Evaluates CISTEM-style precision, recall, and F1-relevant counts.
+     *
+     * @param state shared benchmark state
+     * @param counters quality counters
+     * @param blackhole result sink
+     * @return evaluated token count for this operation
+     * @throws IOException if token filtering cannot run
+     */
+    @Benchmark
+    @Warmup(iterations = 0)
+    @Measurement(iterations = 1, time = 1, timeUnit = java.util.concurrent.TimeUnit.MILLISECONDS)
+    @Fork(0)
+    public long cistemStyleQuality(final SharedState state, final GoldstandardQualityCounters counters,
+            final Blackhole blackhole) throws IOException {
+        final GoldstandardResult result = evaluateCistemStyle(state.corpus, state.allTokens, state.stemmer, blackhole);
+        counters.truePositives += result.truePositives();
+        counters.falsePositives += result.falsePositives();
+        counters.falseNegatives += result.falseNegatives();
+        counters.evaluatedClusters += result.evaluatedClusters();
+        counters.evaluatedTokens += result.evaluatedTokens();
+        return result.evaluatedTokens();
+    }
+
+    /**
+     * Benchmarks candidate throughput over the selected gold standard.
+     *
+     * @param state shared benchmark state
+     * @param blackhole result sink
+     * @throws IOException if token filtering cannot run
+     */
+    @Benchmark
+    public void cistemStyleSpeed(final SharedState state, final Blackhole blackhole) throws IOException {
+        state.stemmer.stem(state.allTokens, blackhole);
+    }
+
+    /**
+     * Named German candidates used for the CISTEM gold-standard comparison.
+     */
+    private enum GermanCandidate {
+        GERMAN_RADIXOR,
+        GERMAN_LUCENE_GERMAN_STEM_FILTER,
+        GERMAN_LUCENE_GERMAN_LIGHT_STEM_FILTER,
+        GERMAN_LUCENE_GERMAN_MINIMAL_STEM_FILTER,
+        GERMAN_CISTEM,
+        SNOWBALL_GERMAN_DIRECT,
+        SNOWBALL_GERMAN_LUCENE_FILTER;
+
+        /**
+         * Creates a candidate evaluator.
+         *
+         * @return stemmer evaluator
+         * @throws IOException if trie resources cannot be loaded
+         */
+        GoldstandardStemmer createEvaluator() throws IOException {
+            return switch (this) {
+                case GERMAN_RADIXOR -> direct(createGermanRadixorStemmer());
+                case GERMAN_LUCENE_GERMAN_STEM_FILTER ->
+                    tokenFilter(input -> new GermanStemFilter(lowercase(input)));
+                case GERMAN_LUCENE_GERMAN_LIGHT_STEM_FILTER ->
+                    tokenFilter(input -> new GermanLightStemFilter(germanNormalize(input)));
+                case GERMAN_LUCENE_GERMAN_MINIMAL_STEM_FILTER ->
+                    tokenFilter(input -> new GermanMinimalStemFilter(germanNormalize(input)));
+                case GERMAN_CISTEM -> direct(Cistem::stem);
+                case SNOWBALL_GERMAN_DIRECT -> direct(SnowballLanguageCase.GERMAN.createDirectStemmer()::stem);
+                case SNOWBALL_GERMAN_LUCENE_FILTER ->
+                    tokenFilter(input -> new SnowballFilter(new LowerCaseFilter(input),
+                            SnowballLanguageCase.GERMAN.luceneSnowballName()));
+            };
+        }
+    }
+
+    /**
+     * Evaluates one full corpus through CISTEM-style cluster scoring.
+     *
+     * <p>
+     * For each cluster, the most frequent predicted stem is considered the
+     * cluster main stem. TP are cluster words mapped to this stem, FN are
+     * words mapped elsewhere inside the same cluster, and FP are words from
+     * other clusters mapped to the same main stem.
+     * </p>
+     *
+     * @param corpus parsed gold standard corpus
+     * @param allTokens flattened token sequence
+     * @param stemmer candidate stemmer
+     * @param blackhole result sink
+     * @return aggregated TP/FP/FN counters and token metrics
+     * @throws IOException when token filtering cannot run
+     */
+    private static GoldstandardResult evaluateCistemStyle(final GermanGoldstandardCorpus corpus,
+            final String[] allTokens, final GoldstandardStemmer stemmer, final Blackhole blackhole) throws IOException {
+        final String[] predicted = stemmer.stem(allTokens, blackhole);
+        final Map<String, Integer> globalPredictions = new LinkedHashMap<>();
+        for (int index = 0; index < allTokens.length; index++) {
+            final String prediction = normalizePrediction(predicted[index], allTokens[index]);
+            globalPredictions.put(prediction, globalPredictions.getOrDefault(prediction, 0) + 1);
+        }
+
+        long truePositives = 0L;
+        long falsePositives = 0L;
+        long falseNegatives = 0L;
+        int tokenOffset = 0;
+        for (final String[] cluster : corpus.clusters()) {
+            if (cluster.length == 0) {
+                continue;
+            }
+
+            final Map<String, Integer> localPredictions = new LinkedHashMap<>();
+            for (int index = 0; index < cluster.length; index++) {
+                final int tokenIndex = tokenOffset + index;
+                final String word = allTokens[tokenIndex];
+                final String prediction = normalizePrediction(predicted[tokenIndex], word);
+                localPredictions.put(prediction, localPredictions.getOrDefault(prediction, 0) + 1);
+            }
+
+            final String mainStem = mostFrequent(localPredictions);
+            final int predictedAsMain = localPredictions.get(mainStem);
+            final int clusterSize = cluster.length;
+            final int falseNegative = clusterSize - predictedAsMain;
+            final int falsePositive = globalPredictions.get(mainStem) - predictedAsMain;
+
+            truePositives += predictedAsMain;
+            falseNegatives += falseNegative;
+            falsePositives += falsePositive;
+            tokenOffset += clusterSize;
+        }
+
+        return new GoldstandardResult(truePositives, falsePositives, falseNegatives, corpus.clusters().length,
+                allTokens.length);
+    }
+
+    /**
+     * Creates a direct evaluator.
+     *
+     * @param stemmer direct word stemmer
+     * @return evaluator
+     */
+    private static GoldstandardStemmer direct(final Stemmer stemmer) {
+        Objects.requireNonNull(stemmer, "stemmer");
+        return (tokens, blackhole) -> {
+            final String[] outputs = new String[tokens.length];
+            for (int index = 0; index < tokens.length; index++) {
+                final String output = stemmer.stem(tokens[index]);
+                outputs[index] = output;
+                blackhole.consume(output);
+            }
+            return outputs;
+        };
+    }
+
+    /**
+     * Creates a TokenFilter evaluator.
+     *
+     * @param factory filter stream factory
+     * @return evaluator
+     */
+    private static GoldstandardStemmer tokenFilter(final Function<TokenStream, TokenStream> factory) {
+        Objects.requireNonNull(factory, "factory");
+        return (tokens, blackhole) -> firstTokenFilterOutputs(tokens, factory, blackhole);
+    }
+
+    /**
+     * Loads and parses one gold standard file from generated JMH resources.
+     *
+     * @param resourceName gold standard file name
+     * @return parsed corpus
+     * @throws IOException if reading fails
+     */
+    private static GermanGoldstandardCorpus loadCorpus(final String resourceName) throws IOException {
+        final ClassLoader classLoader = GermanGoldstandardStemmerComparisonBenchmark.class.getClassLoader();
+        final InputStream resourceStream = classLoader.getResourceAsStream(resourceName);
+        if (resourceStream == null) {
+            throw new IllegalStateException("Missing generated CISTEM gold standard resource: " + resourceName
+                    + ". Run the Gradle JMH resource preparation task to download benchmark-only inputs.");
+        }
+        try (InputStream input = resourceStream) {
+            return parseCorpus(new BufferedReader(new InputStreamReader(input, StandardCharsets.UTF_8)));
+        }
+    }
+
+    /**
+     * Parses CISTEM gold standard format into clustered candidates.
+     *
+     * @param reader UTF-8 reader
+     * @return parsed corpus
+     * @throws IOException if input cannot be read
+     */
+    private static GermanGoldstandardCorpus parseCorpus(final BufferedReader reader) throws IOException {
+        final List<String[]> clusters = new ArrayList<>();
+        String line = reader.readLine();
+        while (line != null) {
+            final String trimmed = line.trim();
+            if (!trimmed.isEmpty()) {
+                final String[] words = trimmed.split("\\s+");
+                if (words.length > 0) {
+                    clusters.add(words);
+                }
+            }
+            line = reader.readLine();
+        }
+        return new GermanGoldstandardCorpus(clusters.toArray(String[][]::new));
+    }
+
+    /**
+     * Flattens the corpus in deterministic cluster order.
+     *
+     * @param corpus corpus to flatten
+     * @return flattened token array
+     */
+    private static String[] flattenCorpusTokens(final GermanGoldstandardCorpus corpus) {
+        int total = 0;
+        for (final String[] cluster : corpus.clusters()) {
+            total += cluster.length;
+        }
+        final String[] tokens = new String[total];
+        int index = 0;
+        for (final String[] cluster : corpus.clusters()) {
+            System.arraycopy(cluster, 0, tokens, index, cluster.length);
+            index += cluster.length;
+        }
+        return tokens;
+    }
+
+    /**
+     * Returns the most frequent key; insertion order is preserved on ties.
+     *
+     * @param frequencies predicted stem frequencies
+     * @return most frequent stem
+     */
+    private static String mostFrequent(final Map<String, Integer> frequencies) {
+        String best = null;
+        int bestCount = -1;
+        for (final Map.Entry<String, Integer> entry : frequencies.entrySet()) {
+            if (entry.getValue() > bestCount) {
+                best = entry.getKey();
+                bestCount = entry.getValue();
+            }
+        }
+        return best;
+    }
+
+    /**
+     * Normalizes a null/empty prediction using the input token as fallback.
+     *
+     * @param prediction stemmed token
+     * @param fallback fallback token
+     * @return safe prediction
+     */
+    private static String normalizePrediction(final String prediction, final String fallback) {
+        if (prediction == null || prediction.isEmpty()) {
+            return fallback;
+        }
+        return prediction;
+    }
+
+    /**
+     * Applies one TokenFilter to all input tokens and returns the first emitted term
+     * for each input token.
+     *
+     * @param tokens input token corpus
+     * @param factory TokenFilter factory
+     * @param blackhole result sink
+     * @return first emitted term per input token
+     * @throws IOException if token streaming fails
+     */
+    private static String[] firstTokenFilterOutputs(final String[] tokens, final Function<TokenStream, TokenStream> factory,
+            final Blackhole blackhole) throws IOException {
+        final String[] outputs = new String[tokens.length];
+        final BenchmarkTokenStream input = new BenchmarkTokenStream(tokens);
+        final TokenStream output = factory.apply(input);
+        final CharTermAttribute termAttribute = output.addAttribute(CharTermAttribute.class);
+        final PositionIncrementAttribute positionAttribute = output.addAttribute(PositionIncrementAttribute.class);
+
+        int inputIndex = -1;
+        boolean recordedForPosition = false;
+        output.reset();
+        while (output.incrementToken()) {
+            final int positionIncrement = positionAttribute.getPositionIncrement();
+            if (positionIncrement > 0) {
+                inputIndex += positionIncrement;
+                recordedForPosition = false;
+            }
+            if (inputIndex >= 0 && inputIndex < outputs.length && !recordedForPosition) {
+                outputs[inputIndex] = termAttribute.toString();
+                blackhole.consume(termAttribute);
+                recordedForPosition = true;
+            }
+        }
+        output.end();
+        output.close();
+
+        for (int index = 0; index < outputs.length; index++) {
+            if (outputs[index] == null) {
+                outputs[index] = tokens[index];
+            }
+        }
+        return outputs;
+    }
+
+    /**
+     * Creates a direct Radixor evaluator using the contracted dictionary trie.
+     *
+     * @return direct Radixor stemmer
+     * @throws IOException if the trie cannot be loaded
+     */
+    private static Stemmer createGermanRadixorStemmer() throws IOException {
+        return new RadixorBenchmarkStemmer(StemmerPatchTrieLoader.loadCompiled(
+                StemmerPatchTrieLoader.Language.DE_DE, true,
+                ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS))::stem;
+    }
+
+    /**
+     * Adds Lucene lower-case normalization.
+     *
+     * @param input token stream
+     * @return normalized token stream
+     */
+    private static TokenStream lowercase(final TokenStream input) {
+        return new LowerCaseFilter(input);
+    }
+
+    /**
+     * Adds Lucene German normalization for light and minimal filters.
+     *
+     * @param input token stream
+     * @return normalized token stream
+     */
+    private static TokenStream germanNormalize(final TokenStream input) {
+        return new GermanNormalizationFilter(lowercase(input));
+    }
+
+    /**
+     * Direct or filter stemmer adapter used by this benchmark.
+     */
+    @FunctionalInterface
+    private interface GoldstandardStemmer {
+
+        /**
+         * Runs one complete token list.
+         *
+         * @param tokens input tokens
+         * @param blackhole result sink
+         * @return per-token outputs
+         * @throws IOException if filter processing fails
+         */
+        String[] stem(String[] tokens, Blackhole blackhole) throws IOException;
+    }
+
+    /**
+     * Deterministic direct word stem function.
+     */
+    @FunctionalInterface
+    private interface Stemmer {
+
+        /**
+         * Stems one token.
+         *
+         * @param token input token
+         * @return stemmed token
+         */
+        String stem(String token);
+    }
+
+    /**
+     * Immutable parsed CISTEM gold standard corpus.
+     */
+    private static final class GermanGoldstandardCorpus {
+
+        private final String[][] clusters;
+
+        GermanGoldstandardCorpus(final String[][] clusters) {
+            this.clusters = clusters;
+        }
+
+        String[][] clusters() {
+            return this.clusters;
+        }
+    }
+
+    /**
+     * Aggregated quality result for one benchmark operation.
+     *
+     * @param truePositives true positives
+     * @param falsePositives false positives
+     * @param falseNegatives false negatives
+     * @param evaluatedClusters evaluated clusters
+     * @param evaluatedTokens evaluated tokens
+     */
+    private record GoldstandardResult(long truePositives, long falsePositives, long falseNegatives,
+            long evaluatedClusters, long evaluatedTokens) {
+    }
+}

src/jmh/java/org/egothor/stemmer/benchmark/HunspellStemmerComparisonBenchmark.java

@@ -0,0 +1,315 @@
+/*******************************************************************************
+ * Copyright (C) 2026, Leo Galambos
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice,
+ *    this list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ *    this list of conditions and the following disclaimer in the documentation
+ *    and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its contributors
+ *    may be used to endorse or promote products derived from this software
+ *    without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+ * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+ * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+ * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+ * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+ * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+ * POSSIBILITY OF SUCH DAMAGE.
+ ******************************************************************************/
+package org.egothor.stemmer.benchmark;
+
+import java.io.IOException;
+import java.io.InputStream;
+import java.text.ParseException;
+import java.util.List;
+import java.util.Locale;
+import java.util.concurrent.TimeUnit;
+
+import org.apache.lucene.analysis.LowerCaseFilter;
+import org.apache.lucene.analysis.TokenStream;
+import org.apache.lucene.analysis.hunspell.Dictionary;
+import org.apache.lucene.analysis.hunspell.HunspellStemFilter;
+import org.apache.lucene.analysis.hunspell.SortingStrategy;
+import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
+import org.egothor.stemmer.CompiledPatchCommand;
+import org.egothor.stemmer.FrequencyTrie;
+import org.egothor.stemmer.ReductionMode;
+import org.egothor.stemmer.StemmerPatchTrieLoader;
+import org.openjdk.jmh.annotations.Benchmark;
+import org.openjdk.jmh.annotations.BenchmarkMode;
+import org.openjdk.jmh.annotations.Level;
+import org.openjdk.jmh.annotations.Measurement;
+import org.openjdk.jmh.annotations.Mode;
+import org.openjdk.jmh.annotations.OutputTimeUnit;
+import org.openjdk.jmh.annotations.Param;
+import org.openjdk.jmh.annotations.Scope;
+import org.openjdk.jmh.annotations.Setup;
+import org.openjdk.jmh.annotations.State;
+import org.openjdk.jmh.annotations.Warmup;
+import org.openjdk.jmh.infra.Blackhole;
+
+/**
+ * Compares Radixor with Lucene's Hunspell integration over selected
+ * benchmark-only Hunspell dictionaries.
+ */
+@BenchmarkMode(Mode.AverageTime)
+@OutputTimeUnit(TimeUnit.NANOSECONDS)
+@Warmup(iterations = 3, time = 1, timeUnit = TimeUnit.SECONDS)
+@Measurement(iterations = 5, time = 1, timeUnit = TimeUnit.SECONDS)
+public class HunspellStemmerComparisonBenchmark {
+
+    /**
+     * Parameterized benchmark case.
+     */
+    @State(Scope.Benchmark)
+    public static class SharedState {
+
+        /**
+         * Selected language case.
+         */
+        @Param({ "ENGLISH", "CZECH", "GERMAN", "SPANISH", "FRENCH", "DUTCH", "POLISH", "UKRAINIAN" })
+        public String languageCaseName;
+
+        /**
+         * Selected language case descriptor.
+         */
+        private HunspellLanguageCase languageCase;
+
+        /**
+         * Shared deterministic changed-token corpus.
+         */
+        private String[] tokens;
+
+        /**
+         * Radixor benchmark adapter.
+         */
+        private RadixorBenchmarkStemmer radixorStemmer;
+
+        /**
+         * Initializes the selected language corpus and Radixor stemmer.
+         *
+         * @throws IOException if the Radixor corpus or trie cannot be loaded
+         */
+        @Setup(Level.Trial)
+        public void setUp() throws IOException {
+            this.languageCase = HunspellLanguageCase.valueOf(this.languageCaseName);
+            this.tokens = LanguageBenchmarkCorpus.createTokens(this.languageCase.radixorLanguage());
+            final FrequencyTrie<CompiledPatchCommand> trie = StemmerPatchTrieLoader.loadCompiled(
+                    this.languageCase.radixorLanguage(), true,
+                    ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
+            this.radixorStemmer = new RadixorBenchmarkStemmer(trie);
+        }
+    }
+
+    /**
+     * Reusable Hunspell filter state.
+     */
+    @State(Scope.Thread)
+    public static class HunspellState {
+
+        /**
+         * Reusable benchmark input stream.
+         */
+        private BenchmarkTokenStream input;
+
+        /**
+         * Reusable Hunspell filter output stream.
+         */
+        private TokenStream output;
+
+        /**
+         * Output term attribute.
+         */
+        private CharTermAttribute termAttribute;
+
+        /**
+         * Initializes the Hunspell dictionary and filter for the selected language.
+         *
+         * @param sharedState selected language state
+         * @throws IOException    if dictionary resources cannot be read
+         * @throws ParseException if the Hunspell dictionary cannot be parsed
+         */
+        @Setup(Level.Trial)
+        public void setUp(final SharedState sharedState) throws IOException, ParseException {
+            this.input = new BenchmarkTokenStream(new String[0]);
+            final Dictionary dictionary = loadDictionary(sharedState.languageCase);
+            this.output = new HunspellStemFilter(new LowerCaseFilter(this.input), dictionary, true);
+            this.termAttribute = this.output.addAttribute(CharTermAttribute.class);
+        }
+
+        /**
+         * Runs Hunspell over one token corpus.
+         *
+         * @param tokens    token corpus
+         * @param blackhole result sink
+         * @throws IOException if Lucene token streaming fails
+         */
+        private void run(final String[] tokens, final Blackhole blackhole) throws IOException {
+            this.input.setTokens(tokens);
+            this.output.reset();
+            while (this.output.incrementToken()) {
+                blackhole.consume(this.termAttribute.toString());
+            }
+            this.output.end();
+        }
+    }
+
+    /**
+     * Runs Radixor direct lookup and patch application.
+     *
+     * @param sharedState selected language state
+     * @param blackhole   result sink
+     */
+    @Benchmark
+    public void radixor(final SharedState sharedState, final Blackhole blackhole) {
+        final String[] tokens = sharedState.tokens;
+        final RadixorBenchmarkStemmer stemmer = sharedState.radixorStemmer;
+        for (String token : tokens) {
+            blackhole.consume(stemmer.stem(token));
+        }
+    }
+
+    /**
+     * Runs Lucene HunspellStemFilter over the selected language corpus.
+     *
+     * @param sharedState selected language state
+     * @param hunspellState reusable Hunspell state
+     * @param blackhole result sink
+     * @throws IOException if Lucene token streaming fails
+     */
+    @Benchmark
+    public void luceneHunspellStemFilter(final SharedState sharedState, final HunspellState hunspellState,
+            final Blackhole blackhole) throws IOException {
+        hunspellState.run(sharedState.tokens, blackhole);
+    }
+
+    /**
+     * Loads a benchmark-only Hunspell dictionary from generated JMH resources.
+     *
+     * @param languageCase selected language case
+     * @return parsed Hunspell dictionary
+     * @throws IOException    if dictionary resources cannot be read
+     * @throws ParseException if the Hunspell dictionary cannot be parsed
+     */
+    private static Dictionary loadDictionary(final HunspellLanguageCase languageCase) throws IOException,
+            ParseException {
+        final ClassLoader classLoader = HunspellStemmerComparisonBenchmark.class.getClassLoader();
+        final String basePath = "hunspell/" + languageCase.hunspellResourceCode() + "/index.";
+        try (InputStream affixStream = openRequiredResource(classLoader, basePath + "aff");
+                InputStream dictionaryStream = openRequiredResource(classLoader, basePath + "dic")) {
+            return new Dictionary(affixStream, List.of(dictionaryStream), true, SortingStrategy.inMemory());
+        }
+    }
+
+    /**
+     * Opens a classpath resource or fails with a descriptive exception.
+     *
+     * @param classLoader class loader
+     * @param path resource path
+     * @return resource stream
+     */
+    private static InputStream openRequiredResource(final ClassLoader classLoader, final String path) {
+        final InputStream stream = classLoader.getResourceAsStream(path);
+        if (stream == null) {
+            throw new IllegalStateException("Missing benchmark-only Hunspell resource: " + path);
+        }
+        return stream;
+    }
+
+    /**
+     * Benchmark language mapping.
+     */
+    private enum HunspellLanguageCase {
+
+        /**
+         * English Hunspell dictionary over the Radixor English corpus.
+         */
+        ENGLISH("en", StemmerPatchTrieLoader.Language.US_UK),
+
+        /**
+         * Czech Hunspell dictionary over the Radixor Czech corpus.
+         */
+        CZECH("cs", StemmerPatchTrieLoader.Language.CS_CZ),
+
+        /**
+         * German Hunspell dictionary over the Radixor German corpus.
+         */
+        GERMAN("de", StemmerPatchTrieLoader.Language.DE_DE),
+
+        /**
+         * Spanish Hunspell dictionary over the Radixor Spanish corpus.
+         */
+        SPANISH("es", StemmerPatchTrieLoader.Language.ES_ES),
+
+        /**
+         * French Hunspell dictionary over the Radixor French corpus.
+         */
+        FRENCH("fr", StemmerPatchTrieLoader.Language.FR_FR),
+
+        /**
+         * Dutch Hunspell dictionary over the Radixor Dutch corpus.
+         */
+        DUTCH("nl", StemmerPatchTrieLoader.Language.NL_NL),
+
+        /**
+         * Polish Hunspell dictionary over the Radixor Polish corpus.
+         */
+        POLISH("pl", StemmerPatchTrieLoader.Language.PL_PL),
+
+        /**
+         * Ukrainian Hunspell dictionary over the Radixor Ukrainian corpus.
+         */
+        UKRAINIAN("uk", StemmerPatchTrieLoader.Language.UK_UA);
+
+        /**
+         * wooorm/dictionaries resource code.
+         */
+        private final String hunspellResourceCode;
+
+        /**
+         * Matching Radixor language.
+         */
+        private final StemmerPatchTrieLoader.Language radixorLanguage;
+
+        /**
+         * Creates a language mapping.
+         *
+         * @param hunspellResourceCode Hunspell resource code
+         * @param radixorLanguage      Radixor language
+         */
+        HunspellLanguageCase(final String hunspellResourceCode, final StemmerPatchTrieLoader.Language radixorLanguage) {
+            this.hunspellResourceCode = hunspellResourceCode.toLowerCase(Locale.ROOT);
+            this.radixorLanguage = radixorLanguage;
+        }
+
+        /**
+         * Returns the Hunspell resource code.
+         *
+         * @return resource code
+         */
+        String hunspellResourceCode() {
+            return this.hunspellResourceCode;
+        }
+
+        /**
+         * Returns the matching Radixor language.
+         *
+         * @return Radixor language
+         */
+        StemmerPatchTrieLoader.Language radixorLanguage() {
+            return this.radixorLanguage;
+        }
+    }
+}

src/jmh/java/org/egothor/stemmer/benchmark/HunspellStemmerComparisonBenchmarkQuality.java

@@ -0,0 +1,436 @@
+/*******************************************************************************
+ * Copyright (C) 2026, Leo Galambos
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice,
+ *    this list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ *    this list of conditions and the following disclaimer in the documentation
+ *    and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its contributors
+ *    may be used to endorse or promote products derived from this software
+ *    without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+ * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+ * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+ * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+ * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+ * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+ * POSSIBILITY OF SUCH DAMAGE.
+ ******************************************************************************/
+package org.egothor.stemmer.benchmark;
+
+import java.io.IOException;
+import java.io.InputStream;
+import java.text.ParseException;
+import java.util.List;
+import java.util.Locale;
+import java.util.Objects;
+import java.util.concurrent.TimeUnit;
+
+import org.apache.lucene.analysis.LowerCaseFilter;
+import org.apache.lucene.analysis.TokenStream;
+import org.apache.lucene.analysis.hunspell.Dictionary;
+import org.apache.lucene.analysis.hunspell.HunspellStemFilter;
+import org.apache.lucene.analysis.hunspell.SortingStrategy;
+import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
+import org.apache.lucene.analysis.tokenattributes.PositionIncrementAttribute;
+import org.egothor.stemmer.StemmerPatchTrieLoader;
+import org.openjdk.jmh.annotations.AuxCounters;
+import org.openjdk.jmh.annotations.Benchmark;
+import org.openjdk.jmh.annotations.BenchmarkMode;
+import org.openjdk.jmh.annotations.Fork;
+import org.openjdk.jmh.annotations.Level;
+import org.openjdk.jmh.annotations.Measurement;
+import org.openjdk.jmh.annotations.Mode;
+import org.openjdk.jmh.annotations.OutputTimeUnit;
+import org.openjdk.jmh.annotations.Param;
+import org.openjdk.jmh.annotations.Scope;
+import org.openjdk.jmh.annotations.Setup;
+import org.openjdk.jmh.annotations.State;
+import org.openjdk.jmh.annotations.Warmup;
+import org.openjdk.jmh.infra.Blackhole;
+
+/**
+ * Emits exact-root agreement metrics for the benchmark-only Hunspell comparisons.
+ *
+ * <p>
+ * This class mirrors the existing Hunspell throughput setup but adds
+ * quality-style accuracy counters for every Hunspell language dictionary used
+ * in benchmark-only throughput comparisons.
+ * </p>
+ */
+@BenchmarkMode(Mode.AverageTime)
+@OutputTimeUnit(TimeUnit.NANOSECONDS)
+@Warmup(iterations = 0)
+@Measurement(iterations = 1, time = 1, timeUnit = TimeUnit.MILLISECONDS)
+@Fork(0)
+public class HunspellStemmerComparisonBenchmarkQuality {
+
+    /**
+     * Shared quality corpus and Hunspell dictionary for a selected language.
+     */
+    @State(Scope.Benchmark)
+    public static class SharedState {
+
+        /**
+         * Selected language case.
+         */
+        @Param({ "ENGLISH", "CZECH", "GERMAN", "SPANISH", "FRENCH", "DUTCH", "POLISH", "UKRAINIAN" })
+        public String languageCaseName;
+
+        /**
+         * Selected language descriptor.
+         */
+        private HunspellLanguageCase languageCase;
+
+        /**
+         * Complete language dictionary corpus and expected roots.
+         */
+        private LanguageBenchmarkCorpus.Corpus corpus;
+
+        /**
+         * Parsed benchmark-only Hunspell dictionary.
+         */
+        private Dictionary dictionary;
+
+        /**
+         * Initializes quality resources.
+         *
+         * @throws IOException    if corpus or dictionary loading fails
+         * @throws ParseException if the Hunspell dictionary cannot be parsed
+         */
+        @Setup(Level.Trial)
+        public void setUp() throws IOException, ParseException {
+            this.languageCase = HunspellLanguageCase.valueOf(this.languageCaseName);
+            this.corpus = LanguageBenchmarkCorpus.createFullCorpus(this.languageCase.radixorLanguage());
+            this.dictionary = loadDictionary(this.languageCase);
+        }
+    }
+
+    /**
+     * JMH auxiliary counters for exact-root agreement.
+     */
+    @State(Scope.Thread)
+    @AuxCounters(AuxCounters.Type.EVENTS)
+    public static class AccuracyCounters {
+
+        /**
+         * Number of exact-root matches.
+         */
+        public long correctMatches;
+
+        /**
+         * Number of evaluated tokens.
+         */
+        public long evaluatedTokens;
+
+        /**
+         * Number of exact-root matches where the input token differs from the
+         * expected root.
+         */
+        public long changedCorrectMatches;
+
+        /**
+         * Number of evaluated tokens where the input token differs from the expected
+         * root.
+         */
+        public long changedEvaluatedTokens;
+
+        /**
+         * Number of exact-root matches where the input token is already the expected
+         * root.
+         */
+        public long rootPreservedMatches;
+
+        /**
+         * Number of evaluated tokens where the input token is already the expected
+         * root.
+         */
+        public long rootEvaluatedTokens;
+
+        /**
+         * Resets counters before each measured iteration.
+         */
+        @Setup(Level.Iteration)
+        public void reset() {
+            this.correctMatches = 0L;
+            this.evaluatedTokens = 0L;
+            this.changedCorrectMatches = 0L;
+            this.changedEvaluatedTokens = 0L;
+            this.rootPreservedMatches = 0L;
+            this.rootEvaluatedTokens = 0L;
+        }
+    }
+
+    /**
+     * Evaluates exact-root agreement for the selected Hunspell dictionary.
+     *
+     * @param sharedState shared quality state
+     * @param counters    JMH auxiliary counters
+     * @param blackhole   result sink
+     * @return exact-root match count
+     * @throws IOException if Lucene token streaming fails
+     */
+    @Benchmark
+    public int luceneHunspellStemFilterAccuracy(final SharedState sharedState, final AccuracyCounters counters,
+            final Blackhole blackhole) throws IOException {
+        final String[] actualStems = firstHunspellOutputs(sharedState.corpus.tokens(), sharedState.dictionary,
+                blackhole);
+        final String[] tokens = sharedState.corpus.tokens();
+        final String[] expectedRoots = sharedState.corpus.expectedRoots();
+
+        int correct = 0;
+        int changedCorrect = 0;
+        int changedEvaluated = 0;
+        int rootPreserved = 0;
+        int rootEvaluated = 0;
+        for (int index = 0; index < actualStems.length; index++) {
+            final String token = tokens[index];
+            final String expectedRoot = expectedRoots[index];
+            final boolean exact = Objects.equals(expectedRoot, actualStems[index]);
+            if (exact) {
+                correct++;
+            }
+            if (Objects.equals(token, expectedRoot)) {
+                rootEvaluated++;
+                if (exact) {
+                    rootPreserved++;
+                }
+            } else {
+                changedEvaluated++;
+                if (exact) {
+                    changedCorrect++;
+                }
+            }
+        }
+
+        counters.correctMatches += correct;
+        counters.evaluatedTokens += actualStems.length;
+        counters.changedCorrectMatches += changedCorrect;
+        counters.changedEvaluatedTokens += changedEvaluated;
+        counters.rootPreservedMatches += rootPreserved;
+        counters.rootEvaluatedTokens += rootEvaluated;
+        return correct;
+    }
+
+    /**
+     * Extracts the first emitted Hunspell stem for each input token.
+     *
+     * @param tokens     token corpus
+     * @param dictionary Hunspell dictionary
+     * @param blackhole  result sink
+     * @return first emitted term per input token
+     * @throws IOException if Lucene streaming fails
+     */
+    private static String[] firstHunspellOutputs(final String[] tokens, final Dictionary dictionary,
+            final Blackhole blackhole) throws IOException {
+        final String[] outputs = new String[tokens.length];
+        final BenchmarkTokenStream input = new BenchmarkTokenStream(tokens);
+        final TokenStream output = new HunspellStemFilter(new LowerCaseFilter(input), dictionary, true);
+        final CharTermAttribute termAttribute = output.addAttribute(CharTermAttribute.class);
+        final PositionIncrementAttribute positionAttribute = output.addAttribute(PositionIncrementAttribute.class);
+        int inputIndex = -1;
+        boolean recordedForPosition = false;
+
+        output.reset();
+        while (output.incrementToken()) {
+            final int positionIncrement = positionAttribute.getPositionIncrement();
+            if (positionIncrement > 0) {
+                inputIndex += positionIncrement;
+                recordedForPosition = false;
+            }
+            if (inputIndex >= 0 && inputIndex < outputs.length && !recordedForPosition) {
+                outputs[inputIndex] = termAttribute.toString();
+                recordedForPosition = true;
+            }
+            if (blackhole != null) {
+                blackhole.consume(termAttribute);
+            }
+        }
+        output.end();
+        output.close();
+
+        for (int index = 0; index < outputs.length; index++) {
+            if (outputs[index] == null) {
+                outputs[index] = tokens[index];
+            }
+        }
+        return outputs;
+    }
+
+    /**
+     * Loads a benchmark-only Hunspell dictionary from generated resources.
+     *
+     * @param languageCase selected language case
+     * @return parsed dictionary
+     * @throws IOException    if dictionary resources cannot be read
+     * @throws ParseException if dictionary parsing fails
+     */
+    private static Dictionary loadDictionary(final HunspellLanguageCase languageCase) throws IOException,
+            ParseException {
+        final ClassLoader classLoader = HunspellStemmerComparisonBenchmarkQuality.class.getClassLoader();
+        final String basePath = "hunspell/" + languageCase.hunspellResourceCode() + "/index.";
+        try (InputStream affixStream = openRequiredResource(classLoader, basePath + "aff");
+                InputStream dictionaryStream = openRequiredResource(classLoader, basePath + "dic")) {
+            return new Dictionary(affixStream, List.of(dictionaryStream), true, SortingStrategy.inMemory());
+        }
+    }
+
+    /**
+     * Stems one analytical batch through the exact Hunspell quality-benchmark path.
+     *
+     * @param languageCase declared Hunspell language case
+     * @param tokens original dictionary forms
+     * @return first Hunspell output per input form
+     * @throws IOException if dictionary parsing or token streaming fails
+     */
+    static String[] stemForQuality(final HunspellLanguageCase languageCase, final String[] tokens) throws IOException {
+        try {
+            return firstHunspellOutputs(tokens, loadDictionary(languageCase), null);
+        } catch (ParseException exception) {
+            throw new IOException("Unable to parse the JMH Hunspell dictionary for " + languageCase + ".", exception);
+        }
+    }
+
+    /** Returns all distinct Hunspell stems per token through the quality-benchmark dictionary. */
+    static List<List<String>> stemCandidatesForQuality(final HunspellLanguageCase languageCase,
+            final String[] tokens) throws IOException {
+        try {
+            final Dictionary dictionary = loadDictionary(languageCase);
+            final List<java.util.LinkedHashSet<String>> candidates = new java.util.ArrayList<>(tokens.length);
+            for (int index = 0; index < tokens.length; index++) { candidates.add(new java.util.LinkedHashSet<>()); }
+            final BenchmarkTokenStream input = new BenchmarkTokenStream(tokens);
+            final TokenStream output = new HunspellStemFilter(new LowerCaseFilter(input), dictionary, true);
+            final CharTermAttribute term = output.addAttribute(CharTermAttribute.class);
+            final PositionIncrementAttribute position = output.addAttribute(PositionIncrementAttribute.class);
+            int inputIndex = -1;
+            output.reset();
+            while (output.incrementToken()) {
+                if (position.getPositionIncrement() > 0) { inputIndex += position.getPositionIncrement(); }
+                if (inputIndex >= 0 && inputIndex < candidates.size()) { candidates.get(inputIndex).add(term.toString()); }
+            }
+            output.end();
+            output.close();
+            final String[] primary = firstHunspellOutputs(tokens, dictionary, null);
+            final List<List<String>> result = new java.util.ArrayList<>(tokens.length);
+            for (int index = 0; index < tokens.length; index++) {
+                candidates.get(index).add(primary[index]);
+                result.add(List.copyOf(candidates.get(index)));
+            }
+            return List.copyOf(result);
+        } catch (ParseException exception) {
+            throw new IOException("Unable to parse the JMH Hunspell dictionary for " + languageCase + ".", exception);
+        }
+    }
+
+    /**
+     * Opens a required classpath resource.
+     *
+     * @param classLoader class loader
+     * @param path        resource path
+     * @return resource stream
+     */
+    private static InputStream openRequiredResource(final ClassLoader classLoader, final String path) {
+        final InputStream stream = classLoader.getResourceAsStream(path);
+        if (stream == null) {
+            throw new IllegalStateException("Missing benchmark-only Hunspell resource: " + path);
+        }
+        return stream;
+    }
+
+    /**
+     * Benchmark language mapping.
+     */
+    enum HunspellLanguageCase {
+
+        /**
+         * English Hunspell dictionary over the Radixor English corpus.
+         */
+        ENGLISH("en", StemmerPatchTrieLoader.Language.US_UK),
+
+        /**
+         * Czech Hunspell dictionary over the Radixor Czech corpus.
+         */
+        CZECH("cs", StemmerPatchTrieLoader.Language.CS_CZ),
+
+        /**
+         * German Hunspell dictionary over the Radixor German corpus.
+         */
+        GERMAN("de", StemmerPatchTrieLoader.Language.DE_DE),
+
+        /**
+         * Spanish Hunspell dictionary over the Radixor Spanish corpus.
+         */
+        SPANISH("es", StemmerPatchTrieLoader.Language.ES_ES),
+
+        /**
+         * French Hunspell dictionary over the Radixor French corpus.
+         */
+        FRENCH("fr", StemmerPatchTrieLoader.Language.FR_FR),
+
+        /**
+         * Dutch Hunspell dictionary over the Radixor Dutch corpus.
+         */
+        DUTCH("nl", StemmerPatchTrieLoader.Language.NL_NL),
+
+        /**
+         * Polish Hunspell dictionary over the Radixor Polish corpus.
+         */
+        POLISH("pl", StemmerPatchTrieLoader.Language.PL_PL),
+
+        /**
+         * Ukrainian Hunspell dictionary over the Radixor Ukrainian corpus.
+         */
+        UKRAINIAN("uk", StemmerPatchTrieLoader.Language.UK_UA);
+
+        /**
+         * Wooorm/dictionaries resource code.
+         */
+        private final String hunspellResourceCode;
+
+        /**
+         * Matching Radixor language.
+         */
+        private final StemmerPatchTrieLoader.Language radixorLanguage;
+
+        /**
+         * Creates a language mapping.
+         *
+         * @param hunspellResourceCode Hunspell resource code
+         * @param radixorLanguage      Radixor language
+         */
+        HunspellLanguageCase(final String hunspellResourceCode, final StemmerPatchTrieLoader.Language radixorLanguage) {
+            this.hunspellResourceCode = hunspellResourceCode.toLowerCase(Locale.ROOT);
+            this.radixorLanguage = radixorLanguage;
+        }
+
+        /**
+         * Returns the Hunspell resource code.
+         *
+         * @return resource code
+         */
+        String hunspellResourceCode() {
+            return this.hunspellResourceCode;
+        }
+
+        /**
+         * Returns the matching Radixor language.
+         *
+         * @return Radixor language
+         */
+        StemmerPatchTrieLoader.Language radixorLanguage() {
+            return this.radixorLanguage;
+        }
+    }
+}

src/jmh/java/org/egothor/stemmer/benchmark/MultiLanguageStemmerComparisonBenchmark.java

@@ -473,6 +473,19 @@ public class MultiLanguageStemmerComparisonBenchmark {
         filterState.germanMinimalStem.run(sharedState.german.tokens, blackhole);
     }
 
+    /**
+     * Runs CISTEM directly over the German corpus.
+     *
+     * @param sharedState shared benchmark state
+     * @param blackhole   result sink
+     */
+    @Benchmark
+    public void germanCistem(final SharedState sharedState, final Blackhole blackhole) {
+        for (final String token : sharedState.german.tokens) {
+            blackhole.consume(Cistem.stem(token));
+        }
+    }
+
     /**
      * Runs Radixor over the Spanish corpus.
      *

src/jmh/java/org/egothor/stemmer/benchmark/QualityStemmerMatrix.java

@@ -0,0 +1,133 @@
+package org.egothor.stemmer.benchmark;
+
+import java.io.IOException;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Objects;
+import java.util.ArrayList;
+import java.util.EnumSet;
+
+import org.egothor.stemmer.StemmerPatchTrieLoader.Language;
+
+/** Authoritative analytical view of the candidate matrix defined by the JMH quality benchmark. */
+public final class QualityStemmerMatrix {
+    /** Utility class. */
+    private QualityStemmerMatrix() {
+        throw new AssertionError("No instances.");
+    }
+
+    /**
+     * Returns every currently registered JMH quality candidate in declaration order.
+     * The returned list is immutable and is derived directly from the benchmark enum.
+     *
+     * @return complete immutable candidate list
+     */
+    public static List<Candidate> candidates() {
+        final List<Candidate> candidates = new ArrayList<>();
+        Arrays.stream(StemmerComparisonBenchmarkQuality.QualityCandidate.values())
+                .map(candidate -> new Candidate(candidate.name(), candidate.radixorLanguage(),
+                        () -> adapt(candidate.createStemmer())))
+                .forEach(candidates::add);
+        final EnumSet<Language> registeredRadixorLanguages = candidates.stream()
+                .filter(candidate -> candidate.name().endsWith("_RADIXOR"))
+                .map(Candidate::language).collect(() -> EnumSet.noneOf(Language.class), EnumSet::add, EnumSet::addAll);
+        Arrays.stream(Language.values()).filter(language -> !registeredRadixorLanguages.contains(language))
+                .map(language -> new Candidate(language.name() + "_RADIXOR", language,
+                        () -> adapt(StemmerComparisonBenchmarkQuality.createRadixorQualityStemmer(language))))
+                .forEach(candidates::add);
+        Arrays.stream(HunspellStemmerComparisonBenchmarkQuality.HunspellLanguageCase.values())
+                .map(languageCase -> new Candidate("HUNSPELL_" + languageCase.name() + "_LUCENE_FILTER",
+                        languageCase.radixorLanguage(),
+                        () -> new BatchStemmer() {
+                            /** {@inheritDoc} */
+                            @Override public String[] stem(final String[] forms) throws IOException {
+                                return HunspellStemmerComparisonBenchmarkQuality.stemForQuality(languageCase, forms);
+                            }
+                            /** {@inheritDoc} */
+                            @Override public List<List<String>> stemCandidates(final String[] forms) throws IOException {
+                                return HunspellStemmerComparisonBenchmarkQuality.stemCandidatesForQuality(languageCase, forms);
+                            }
+                            /** {@inheritDoc} */
+                            @Override public boolean supportsMultipleOutputs() { return true; }
+                        }))
+                .forEach(candidates::add);
+        return List.copyOf(candidates);
+    }
+
+    /** Adapts one authoritative general-matrix stemmer without changing capability semantics. */
+    private static BatchStemmer adapt(final StemmerComparisonBenchmarkQuality.CandidateStemmer stemmer) {
+        return new BatchStemmer() {
+            /** {@inheritDoc} */
+            @Override public String[] stem(final String[] forms) throws IOException { return stemmer.stem(forms); }
+            /** {@inheritDoc} */
+            @Override public List<List<String>> stemCandidates(final String[] forms) throws IOException {
+                return stemmer.stemCandidates(forms);
+            }
+            /** {@inheritDoc} */
+            @Override public boolean supportsMultipleOutputs() { return stemmer.supportsMultipleOutputs(); }
+        };
+    }
+
+    /** One JMH candidate and its authoritative dictionary-language mapping. */
+    public static final class Candidate {
+        private final String name;
+        private final Language language;
+        private final StemmerFactory factory;
+
+        /** Creates an immutable facade over one benchmark candidate. */
+        private Candidate(final String name, final Language language,
+                final StemmerFactory factory) {
+            this.name = Objects.requireNonNull(name, "name");
+            this.language = Objects.requireNonNull(language, "language");
+            this.factory = Objects.requireNonNull(factory, "factory");
+        }
+
+        /** @return stable JMH candidate name */
+        public String name() {
+            return this.name;
+        }
+
+        /** @return registered Radixor gold-standard dictionary language */
+        public Language language() {
+            return this.language;
+        }
+
+        /**
+         * Creates a scenario-confined adapter using exactly the JMH factory and preprocessing path.
+         *
+         * @return sequential batch stemmer
+         * @throws IOException if benchmark-only resources cannot be loaded
+         */
+        public BatchStemmer createStemmer() throws IOException {
+            return this.factory.create();
+        }
+    }
+
+    /** Internal checked factory shared by the JMH quality registries. */
+    @FunctionalInterface
+    private interface StemmerFactory {
+        /** @return a scenario-confined adapter @throws IOException if resources fail */
+        BatchStemmer create() throws IOException;
+    }
+
+    /** Sequential, scenario-confined batch stemmer contract. */
+    @FunctionalInterface
+    public interface BatchStemmer {
+        /**
+         * Stems all supplied forms in order.
+         *
+         * @param forms input forms, never {@code null}
+         * @return one non-null output per form
+         * @throws IOException when the JMH adapter fails
+         */
+        String[] stem(String[] forms) throws IOException;
+
+        /** Returns complete candidate sets; single-output adapters return singleton sets. */
+        default List<List<String>> stemCandidates(final String[] forms) throws IOException {
+            return Arrays.stream(stem(forms)).map(List::of).toList();
+        }
+
+        /** @return whether this adapter exposes genuine alternative outputs */
+        default boolean supportsMultipleOutputs() { return false; }
+    }
+}

src/jmh/java/org/egothor/stemmer/benchmark/RadixorBenchmarkStemmer.java

@@ -30,7 +30,10 @@
  ******************************************************************************/
 package org.egothor.stemmer.benchmark;
 
+import java.util.LinkedHashSet;
+import java.util.List;
 import java.util.Objects;
+import java.util.Set;
 
 import org.egothor.stemmer.CompiledPatchCommand;
 import org.egothor.stemmer.FrequencyTrie;
@@ -79,4 +82,22 @@ final class RadixorBenchmarkStemmer {
         }
         return patch.apply(token);
     }
+
+    /**
+     * Returns every distinct candidate stem from the ranked {@code getAll} path,
+     * always including the deterministic primary output.
+     *
+     * @param token original input token
+     * @return immutable candidate list in deterministic ranked order
+     */
+    List<String> stemAll(final String token) {
+        final String primary = stem(token);
+        final Set<String> candidates = new LinkedHashSet<>();
+        candidates.add(primary);
+        final CompiledPatchCommand[] patches = this.trie.getAll(token);
+        for (CompiledPatchCommand patch : patches) {
+            candidates.add(patch.preservesAllSources() ? token : patch.apply(token));
+        }
+        return List.copyOf(candidates);
+    }
 }

src/jmh/java/org/egothor/stemmer/benchmark/StemmerComparisonBenchmarkQuality.java

@@ -144,6 +144,7 @@ public class StemmerComparisonBenchmarkQuality {
                 "GERMAN_LUCENE_GERMAN_STEM_FILTER",
                 "GERMAN_LUCENE_GERMAN_LIGHT_STEM_FILTER",
                 "GERMAN_LUCENE_GERMAN_MINIMAL_STEM_FILTER",
+                "GERMAN_CISTEM",
                 "SPANISH_RADIXOR",
                 "SPANISH_LUCENE_SPANISH_LIGHT_STEM_FILTER",
                 "SPANISH_LUCENE_SPANISH_MINIMAL_STEM_FILTER",
@@ -178,46 +179,32 @@ public class StemmerComparisonBenchmarkQuality {
                 "UKRAINIAN_RADIXOR",
                 "UKRAINIAN_MORFOLOGIK_DIRECT",
                 "UKRAINIAN_LUCENE_MORFOLOGIK_FILTER",
-                "SNOWBALL_DANISH_RADIXOR",
                 "SNOWBALL_DANISH_DIRECT",
                 "SNOWBALL_DANISH_LUCENE_FILTER",
-                "SNOWBALL_DUTCH_RADIXOR",
                 "SNOWBALL_DUTCH_DIRECT",
                 "SNOWBALL_DUTCH_LUCENE_FILTER",
-                "SNOWBALL_FINNISH_RADIXOR",
                 "SNOWBALL_FINNISH_DIRECT",
                 "SNOWBALL_FINNISH_LUCENE_FILTER",
-                "SNOWBALL_FRENCH_RADIXOR",
                 "SNOWBALL_FRENCH_DIRECT",
                 "SNOWBALL_FRENCH_LUCENE_FILTER",
-                "SNOWBALL_GERMAN_RADIXOR",
                 "SNOWBALL_GERMAN_DIRECT",
                 "SNOWBALL_GERMAN_LUCENE_FILTER",
-                "SNOWBALL_HUNGARIAN_RADIXOR",
                 "SNOWBALL_HUNGARIAN_DIRECT",
                 "SNOWBALL_HUNGARIAN_LUCENE_FILTER",
-                "SNOWBALL_ITALIAN_RADIXOR",
                 "SNOWBALL_ITALIAN_DIRECT",
                 "SNOWBALL_ITALIAN_LUCENE_FILTER",
-                "SNOWBALL_NORWEGIAN_BOKMAL_RADIXOR",
                 "SNOWBALL_NORWEGIAN_BOKMAL_DIRECT",
                 "SNOWBALL_NORWEGIAN_BOKMAL_LUCENE_FILTER",
-                "SNOWBALL_NORWEGIAN_NYNORSK_RADIXOR",
                 "SNOWBALL_NORWEGIAN_NYNORSK_DIRECT",
                 "SNOWBALL_NORWEGIAN_NYNORSK_LUCENE_FILTER",
-                "SNOWBALL_PORTUGUESE_RADIXOR",
                 "SNOWBALL_PORTUGUESE_DIRECT",
                 "SNOWBALL_PORTUGUESE_LUCENE_FILTER",
-                "SNOWBALL_RUSSIAN_RADIXOR",
                 "SNOWBALL_RUSSIAN_DIRECT",
                 "SNOWBALL_RUSSIAN_LUCENE_FILTER",
-                "SNOWBALL_SPANISH_RADIXOR",
                 "SNOWBALL_SPANISH_DIRECT",
                 "SNOWBALL_SPANISH_LUCENE_FILTER",
-                "SNOWBALL_SWEDISH_RADIXOR",
                 "SNOWBALL_SWEDISH_DIRECT",
                 "SNOWBALL_SWEDISH_LUCENE_FILTER",
-                "SNOWBALL_YIDDISH_RADIXOR",
                 "SNOWBALL_YIDDISH_DIRECT",
                 "SNOWBALL_YIDDISH_LUCENE_FILTER"
         })
@@ -326,7 +313,7 @@ public class StemmerComparisonBenchmarkQuality {
     /**
      * Candidate stemmers that can be evaluated against a Radixor resource.
      */
-    private enum QualityCandidate {
+    enum QualityCandidate {
         ENGLISH_RADIXOR(StemmerPatchTrieLoader.Language.US_UK),
         ENGLISH_SNOWBALL_ORIGINAL_PORTER(StemmerPatchTrieLoader.Language.US_UK),
         ENGLISH_SNOWBALL_PORTER2(StemmerPatchTrieLoader.Language.US_UK),
@@ -343,6 +330,7 @@ public class StemmerComparisonBenchmarkQuality {
         GERMAN_LUCENE_GERMAN_STEM_FILTER(StemmerPatchTrieLoader.Language.DE_DE),
         GERMAN_LUCENE_GERMAN_LIGHT_STEM_FILTER(StemmerPatchTrieLoader.Language.DE_DE),
         GERMAN_LUCENE_GERMAN_MINIMAL_STEM_FILTER(StemmerPatchTrieLoader.Language.DE_DE),
+        GERMAN_CISTEM(StemmerPatchTrieLoader.Language.DE_DE),
         SPANISH_RADIXOR(StemmerPatchTrieLoader.Language.ES_ES),
         SPANISH_LUCENE_SPANISH_LIGHT_STEM_FILTER(StemmerPatchTrieLoader.Language.ES_ES),
         SPANISH_LUCENE_SPANISH_MINIMAL_STEM_FILTER(StemmerPatchTrieLoader.Language.ES_ES),
@@ -377,52 +365,36 @@ public class StemmerComparisonBenchmarkQuality {
         UKRAINIAN_RADIXOR(StemmerPatchTrieLoader.Language.UK_UA),
         UKRAINIAN_MORFOLOGIK_DIRECT(StemmerPatchTrieLoader.Language.UK_UA),
         UKRAINIAN_LUCENE_MORFOLOGIK_FILTER(StemmerPatchTrieLoader.Language.UK_UA),
-        SNOWBALL_DANISH_RADIXOR(StemmerPatchTrieLoader.Language.DA_DK, SnowballLanguageCase.DANISH),
         SNOWBALL_DANISH_DIRECT(StemmerPatchTrieLoader.Language.DA_DK, SnowballLanguageCase.DANISH),
         SNOWBALL_DANISH_LUCENE_FILTER(StemmerPatchTrieLoader.Language.DA_DK, SnowballLanguageCase.DANISH),
-        SNOWBALL_DUTCH_RADIXOR(StemmerPatchTrieLoader.Language.NL_NL, SnowballLanguageCase.DUTCH),
         SNOWBALL_DUTCH_DIRECT(StemmerPatchTrieLoader.Language.NL_NL, SnowballLanguageCase.DUTCH),
         SNOWBALL_DUTCH_LUCENE_FILTER(StemmerPatchTrieLoader.Language.NL_NL, SnowballLanguageCase.DUTCH),
-        SNOWBALL_FINNISH_RADIXOR(StemmerPatchTrieLoader.Language.FI_FI, SnowballLanguageCase.FINNISH),
         SNOWBALL_FINNISH_DIRECT(StemmerPatchTrieLoader.Language.FI_FI, SnowballLanguageCase.FINNISH),
         SNOWBALL_FINNISH_LUCENE_FILTER(StemmerPatchTrieLoader.Language.FI_FI, SnowballLanguageCase.FINNISH),
-        SNOWBALL_FRENCH_RADIXOR(StemmerPatchTrieLoader.Language.FR_FR, SnowballLanguageCase.FRENCH),
         SNOWBALL_FRENCH_DIRECT(StemmerPatchTrieLoader.Language.FR_FR, SnowballLanguageCase.FRENCH),
         SNOWBALL_FRENCH_LUCENE_FILTER(StemmerPatchTrieLoader.Language.FR_FR, SnowballLanguageCase.FRENCH),
-        SNOWBALL_GERMAN_RADIXOR(StemmerPatchTrieLoader.Language.DE_DE, SnowballLanguageCase.GERMAN),
         SNOWBALL_GERMAN_DIRECT(StemmerPatchTrieLoader.Language.DE_DE, SnowballLanguageCase.GERMAN),
         SNOWBALL_GERMAN_LUCENE_FILTER(StemmerPatchTrieLoader.Language.DE_DE, SnowballLanguageCase.GERMAN),
-        SNOWBALL_HUNGARIAN_RADIXOR(StemmerPatchTrieLoader.Language.HU_HU, SnowballLanguageCase.HUNGARIAN),
         SNOWBALL_HUNGARIAN_DIRECT(StemmerPatchTrieLoader.Language.HU_HU, SnowballLanguageCase.HUNGARIAN),
         SNOWBALL_HUNGARIAN_LUCENE_FILTER(StemmerPatchTrieLoader.Language.HU_HU, SnowballLanguageCase.HUNGARIAN),
-        SNOWBALL_ITALIAN_RADIXOR(StemmerPatchTrieLoader.Language.IT_IT, SnowballLanguageCase.ITALIAN),
         SNOWBALL_ITALIAN_DIRECT(StemmerPatchTrieLoader.Language.IT_IT, SnowballLanguageCase.ITALIAN),
         SNOWBALL_ITALIAN_LUCENE_FILTER(StemmerPatchTrieLoader.Language.IT_IT, SnowballLanguageCase.ITALIAN),
-        SNOWBALL_NORWEGIAN_BOKMAL_RADIXOR(StemmerPatchTrieLoader.Language.NB_NO,
-                SnowballLanguageCase.NORWEGIAN_BOKMAL),
         SNOWBALL_NORWEGIAN_BOKMAL_DIRECT(StemmerPatchTrieLoader.Language.NB_NO,
                 SnowballLanguageCase.NORWEGIAN_BOKMAL),
         SNOWBALL_NORWEGIAN_BOKMAL_LUCENE_FILTER(StemmerPatchTrieLoader.Language.NB_NO,
                 SnowballLanguageCase.NORWEGIAN_BOKMAL),
-        SNOWBALL_NORWEGIAN_NYNORSK_RADIXOR(StemmerPatchTrieLoader.Language.NN_NO,
-                SnowballLanguageCase.NORWEGIAN_NYNORSK),
         SNOWBALL_NORWEGIAN_NYNORSK_DIRECT(StemmerPatchTrieLoader.Language.NN_NO,
                 SnowballLanguageCase.NORWEGIAN_NYNORSK),
         SNOWBALL_NORWEGIAN_NYNORSK_LUCENE_FILTER(StemmerPatchTrieLoader.Language.NN_NO,
                 SnowballLanguageCase.NORWEGIAN_NYNORSK),
-        SNOWBALL_PORTUGUESE_RADIXOR(StemmerPatchTrieLoader.Language.PT_PT, SnowballLanguageCase.PORTUGUESE),
         SNOWBALL_PORTUGUESE_DIRECT(StemmerPatchTrieLoader.Language.PT_PT, SnowballLanguageCase.PORTUGUESE),
         SNOWBALL_PORTUGUESE_LUCENE_FILTER(StemmerPatchTrieLoader.Language.PT_PT, SnowballLanguageCase.PORTUGUESE),
-        SNOWBALL_RUSSIAN_RADIXOR(StemmerPatchTrieLoader.Language.RU_RU, SnowballLanguageCase.RUSSIAN),
         SNOWBALL_RUSSIAN_DIRECT(StemmerPatchTrieLoader.Language.RU_RU, SnowballLanguageCase.RUSSIAN),
         SNOWBALL_RUSSIAN_LUCENE_FILTER(StemmerPatchTrieLoader.Language.RU_RU, SnowballLanguageCase.RUSSIAN),
-        SNOWBALL_SPANISH_RADIXOR(StemmerPatchTrieLoader.Language.ES_ES, SnowballLanguageCase.SPANISH),
         SNOWBALL_SPANISH_DIRECT(StemmerPatchTrieLoader.Language.ES_ES, SnowballLanguageCase.SPANISH),
         SNOWBALL_SPANISH_LUCENE_FILTER(StemmerPatchTrieLoader.Language.ES_ES, SnowballLanguageCase.SPANISH),
-        SNOWBALL_SWEDISH_RADIXOR(StemmerPatchTrieLoader.Language.SV_SE, SnowballLanguageCase.SWEDISH),
         SNOWBALL_SWEDISH_DIRECT(StemmerPatchTrieLoader.Language.SV_SE, SnowballLanguageCase.SWEDISH),
         SNOWBALL_SWEDISH_LUCENE_FILTER(StemmerPatchTrieLoader.Language.SV_SE, SnowballLanguageCase.SWEDISH),
-        SNOWBALL_YIDDISH_RADIXOR(StemmerPatchTrieLoader.Language.YI, SnowballLanguageCase.YIDDISH),
         SNOWBALL_YIDDISH_DIRECT(StemmerPatchTrieLoader.Language.YI, SnowballLanguageCase.YIDDISH),
         SNOWBALL_YIDDISH_LUCENE_FILTER(StemmerPatchTrieLoader.Language.YI, SnowballLanguageCase.YIDDISH);
 
@@ -472,9 +444,9 @@ public class StemmerComparisonBenchmarkQuality {
          * @return quality evaluator
          * @throws IOException if stemmer resources cannot be loaded
          */
-        QualityEvaluator createEvaluator() throws IOException {
+        CandidateStemmer createStemmer() throws IOException {
             if (name().endsWith("_RADIXOR")) {
-                return direct(createRadixorStemmer(this.radixorLanguage));
+                return radixor(createRadixorStemmer(this.radixorLanguage));
             }
             if (name().endsWith("_DIRECT") && this.snowballLanguageCase != null) {
                 return direct(this.snowballLanguageCase.createDirectStemmer()::stem);
@@ -504,6 +476,7 @@ public class StemmerComparisonBenchmarkQuality {
                     tokenFilter(input -> new GermanLightStemFilter(germanNormalize(input)));
                 case GERMAN_LUCENE_GERMAN_MINIMAL_STEM_FILTER ->
                     tokenFilter(input -> new GermanMinimalStemFilter(germanNormalize(input)));
+                case GERMAN_CISTEM -> direct(createGermanCistemStemmer());
                 case SPANISH_LUCENE_SPANISH_LIGHT_STEM_FILTER ->
                     tokenFilter(input -> new SpanishLightStemFilter(lowercase(input)));
                 case SPANISH_LUCENE_SPANISH_MINIMAL_STEM_FILTER ->
@@ -535,7 +508,7 @@ public class StemmerComparisonBenchmarkQuality {
                 }
                 case POLISH_LUCENE_STEMPEL_FILTER ->
                     tokenFilter(input -> new StempelFilter(input, new StempelStemmer(PolishAnalyzer.getDefaultTable())));
-                case POLISH_LUCENE_MORFOLOGIK_FILTER -> tokenFilter(MorfologikFilter::new);
+                case POLISH_LUCENE_MORFOLOGIK_FILTER -> tokenFilter(MorfologikFilter::new, true);
                 case PORTUGUESE_LUCENE_PORTUGUESE_STEM_FILTER ->
                     tokenFilter(input -> new PortugueseStemFilter(lowercase(input)));
                 case PORTUGUESE_LUCENE_PORTUGUESE_LIGHT_STEM_FILTER ->
@@ -550,15 +523,29 @@ public class StemmerComparisonBenchmarkQuality {
                     tokenFilter(input -> new SwedishMinimalStemFilter(lowercase(input)));
                 case UKRAINIAN_MORFOLOGIK_DIRECT -> {
                     final DictionaryLookup lookup = new DictionaryLookup(loadUkrainianMorfologikDictionary());
-                    yield direct(token -> firstMorfologikStem(token, lookup));
+                    yield morphologik(lookup);
                 }
                 case UKRAINIAN_LUCENE_MORFOLOGIK_FILTER -> {
                     final Dictionary dictionary = loadUkrainianMorfologikDictionary();
-                    yield tokenFilter(input -> new MorfologikFilter(input, dictionary));
+                    yield tokenFilter(input -> new MorfologikFilter(input, dictionary), true);
                 }
                 default -> throw new IllegalStateException("No evaluator for " + this + ".");
             };
         }
+
+        /** Creates the exact-root evaluator used by the JMH quality benchmark. */
+        QualityEvaluator createEvaluator() throws IOException {
+            return exactRootEvaluator(createStemmer());
+        }
+    }
+
+    /**
+     * Creates a CISTEM stemmer adapter.
+     *
+     * @return German stem function
+     */
+    private static Stemmer createGermanCistemStemmer() {
+        return Cistem::stem;
     }
 
     /**
@@ -593,6 +580,68 @@ public class StemmerComparisonBenchmarkQuality {
         QualityResult evaluate(LanguageBenchmarkCorpus.Corpus corpus, Blackhole blackhole) throws IOException;
     }
 
+    /** Stateful candidate adapter confined to one sequential evaluation scenario. */
+    @FunctionalInterface
+    interface CandidateStemmer {
+
+        /**
+         * Stems a deterministic batch through the authoritative JMH invocation path.
+         *
+         * @param tokens input tokens, never {@code null}
+         * @return one non-null output for every input token
+         * @throws IOException if a token-stream implementation fails
+         */
+        String[] stem(String[] tokens) throws IOException;
+
+        /**
+         * Returns complete distinct candidate sets, each containing its primary output.
+         * Single-output adapters expose singleton lists.
+         *
+         * @param tokens input tokens
+         * @return immutable candidate list for every token
+         * @throws IOException if adapter processing fails
+         */
+        default List<List<String>> stemCandidates(final String[] tokens) throws IOException {
+            final String[] primary = stem(tokens);
+            return java.util.Arrays.stream(primary).map(List::of).toList();
+        }
+
+        /** @return whether the adapter exposes genuine alternative outputs */
+        default boolean supportsMultipleOutputs() {
+            return false;
+        }
+    }
+
+    /** Creates the candidate-capable Radixor adapter backed by ranked {@code getAll}. */
+    private static CandidateStemmer radixor(final RadixorBenchmarkStemmer stemmer) {
+        return new CandidateStemmer() {
+            /** {@inheritDoc} */
+            @Override public String[] stem(final String[] tokens) {
+                final String[] outputs = new String[tokens.length];
+                for (int index = 0; index < tokens.length; index++) { outputs[index] = stemmer.stem(tokens[index]); }
+                return outputs;
+            }
+            /** {@inheritDoc} */
+            @Override public List<List<String>> stemCandidates(final String[] tokens) {
+                return java.util.Arrays.stream(tokens).map(stemmer::stemAll).toList();
+            }
+            /** {@inheritDoc} */
+            @Override public boolean supportsMultipleOutputs() { return true; }
+        };
+    }
+
+    /**
+     * Creates the authoritative multi-output Radixor adapter for a validated dictionary language.
+     *
+     * @param language bundled Radixor language
+     * @return scenario-confined adapter using the JMH invocation path
+     * @throws IOException if the compiled dictionary cannot be loaded
+     */
+    static CandidateStemmer createRadixorQualityStemmer(final StemmerPatchTrieLoader.Language language)
+            throws IOException {
+        return radixor(createRadixorStemmer(language));
+    }
+
     /**
      * Exact-root agreement counters for one quality operation.
      *
@@ -608,57 +657,79 @@ public class StemmerComparisonBenchmarkQuality {
     }
 
     /**
-     * Creates a direct evaluator.
+     * Creates a direct candidate adapter.
      *
      * @param stemmer direct stemmer
-     * @return quality evaluator
+     * @return sequential batch adapter
      */
-    private static QualityEvaluator direct(final Stemmer stemmer) {
+    private static CandidateStemmer direct(final Stemmer stemmer) {
         Objects.requireNonNull(stemmer, "stemmer");
-        return (corpus, blackhole) -> {
+        return tokens -> {
-            int correct = 0;
+            final String[] outputs = new String[tokens.length];
-            int changedCorrect = 0;
-            int changedEvaluated = 0;
-            int rootPreserved = 0;
-            int rootEvaluated = 0;
-            final String[] tokens = corpus.tokens();
-            final String[] expectedRoots = corpus.expectedRoots();
             for (int index = 0; index < tokens.length; index++) {
-                final String token = tokens[index];
+                outputs[index] = stemmer.stem(tokens[index]);
-                final String expectedRoot = expectedRoots[index];
-                final String actual = stemmer.stem(token);
-                blackhole.consume(actual);
-                final boolean exact = Objects.equals(expectedRoot, actual);
-                if (exact) {
-                    correct++;
-                }
-                if (Objects.equals(token, expectedRoot)) {
-                    rootEvaluated++;
-                    if (exact) {
-                        rootPreserved++;
-                    }
-                } else {
-                    changedEvaluated++;
-                    if (exact) {
-                        changedCorrect++;
-                    }
-                }
             }
-            return new QualityResult(correct, tokens.length, changedCorrect, changedEvaluated, rootPreserved,
+            return outputs;
-                    rootEvaluated);
         };
     }
 
     /**
-     * Creates a TokenFilter evaluator.
+     * Creates a TokenFilter candidate adapter.
      *
      * @param factory token stream factory
-     * @return quality evaluator
+     * @return sequential batch adapter
      */
-    private static QualityEvaluator tokenFilter(final Function<TokenStream, TokenStream> factory) {
+    private static CandidateStemmer tokenFilter(final Function<TokenStream, TokenStream> factory) {
         Objects.requireNonNull(factory, "factory");
+        return tokens -> firstTokenFilterOutputs(tokens, factory, null);
+    }
+
+    /** Creates a TokenFilter adapter that preserves all terms emitted per position. */
+    private static CandidateStemmer tokenFilter(final Function<TokenStream, TokenStream> factory,
+            final boolean multipleOutputs) {
+        if (!multipleOutputs) { return tokenFilter(factory); }
+        return new CandidateStemmer() {
+            /** {@inheritDoc} */
+            @Override public String[] stem(final String[] tokens) throws IOException {
+                return firstTokenFilterOutputs(tokens, factory, null);
+            }
+            /** {@inheritDoc} */
+            @Override public List<List<String>> stemCandidates(final String[] tokens) throws IOException {
+                return allTokenFilterOutputs(tokens, factory);
+            }
+            /** {@inheritDoc} */
+            @Override public boolean supportsMultipleOutputs() { return true; }
+        };
+    }
+
+    /** Creates a multi-analysis Morphologik direct adapter. */
+    private static CandidateStemmer morphologik(final DictionaryLookup lookup) {
+        return new CandidateStemmer() {
+            /** {@inheritDoc} */
+            @Override public String[] stem(final String[] tokens) {
+                final String[] outputs = new String[tokens.length];
+                for (int index = 0; index < tokens.length; index++) { outputs[index] = firstMorfologikStem(tokens[index], lookup); }
+                return outputs;
+            }
+            /** {@inheritDoc} */
+            @Override public List<List<String>> stemCandidates(final String[] tokens) {
+                return java.util.Arrays.stream(tokens).map(token -> allMorfologikStems(token, lookup)).toList();
+            }
+            /** {@inheritDoc} */
+            @Override public boolean supportsMultipleOutputs() { return true; }
+        };
+    }
+
+    /**
+     * Creates exact-root accounting around an authoritative candidate adapter.
+     *
+     * @param stemmer candidate adapter
+     * @return JMH exact-root evaluator
+     */
+    private static QualityEvaluator exactRootEvaluator(final CandidateStemmer stemmer) {
+        Objects.requireNonNull(stemmer, "stemmer");
         return (corpus, blackhole) -> {
-            final String[] actualStems = firstTokenFilterOutputs(corpus.tokens(), factory, blackhole);
+            final String[] actualStems = stemmer.stem(corpus.tokens());
             final String[] expectedRoots = corpus.expectedRoots();
             final String[] tokens = corpus.tokens();
             int correct = 0;
@@ -669,6 +740,7 @@ public class StemmerComparisonBenchmarkQuality {
             for (int index = 0; index < actualStems.length; index++) {
                 final String token = tokens[index];
                 final String expectedRoot = expectedRoots[index];
+                blackhole.consume(actualStems[index]);
                 final boolean exact = Objects.equals(expectedRoot, actualStems[index]);
                 if (exact) {
                     correct++;
@@ -697,10 +769,9 @@ public class StemmerComparisonBenchmarkQuality {
      * @return direct stemmer
      * @throws IOException if the trie cannot be loaded
      */
-    private static Stemmer createRadixorStemmer(final StemmerPatchTrieLoader.Language language) throws IOException {
+    private static RadixorBenchmarkStemmer createRadixorStemmer(final StemmerPatchTrieLoader.Language language) throws IOException {
-        final RadixorBenchmarkStemmer stemmer = new RadixorBenchmarkStemmer(StemmerPatchTrieLoader.loadCompiled(
+        return new RadixorBenchmarkStemmer(StemmerPatchTrieLoader.loadCompiled(
                 language, true, ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS));
-        return stemmer::stem;
     }
 
     /**
@@ -733,6 +804,14 @@ public class StemmerComparisonBenchmarkQuality {
         return analyses.get(0).getStem().toString();
     }
 
+    /** Returns all distinct Morphologik lemma strings and always includes the primary output. */
+    private static List<String> allMorfologikStems(final String token, final DictionaryLookup lookup) {
+        final java.util.LinkedHashSet<String> stems = new java.util.LinkedHashSet<>();
+        stems.add(firstMorfologikStem(token, lookup));
+        for (WordData analysis : lookup.lookup(token)) { stems.add(analysis.getStem().toString()); }
+        return List.copyOf(stems);
+    }
+
     /**
      * Extracts the first emitted term for each input token from a TokenFilter
      * pipeline.
@@ -764,7 +843,9 @@ public class StemmerComparisonBenchmarkQuality {
                 outputs[inputIndex] = termAttribute.toString();
                 recordedForPosition = true;
             }
-            blackhole.consume(termAttribute);
+            if (blackhole != null) {
+                blackhole.consume(termAttribute);
+            }
         }
         output.end();
         output.close();
@@ -777,6 +858,35 @@ public class StemmerComparisonBenchmarkQuality {
         return outputs;
     }
 
+    /**
+     * Extracts every distinct emitted term for each input position and includes the
+     * deterministic primary output even when a filter omits it.
+     */
+    private static List<List<String>> allTokenFilterOutputs(final String[] tokens,
+            final Function<TokenStream, TokenStream> factory) throws IOException {
+        final List<java.util.LinkedHashSet<String>> candidates = new java.util.ArrayList<>(tokens.length);
+        for (int index = 0; index < tokens.length; index++) { candidates.add(new java.util.LinkedHashSet<>()); }
+        final BenchmarkTokenStream input = new BenchmarkTokenStream(tokens);
+        final TokenStream output = factory.apply(input);
+        final CharTermAttribute term = output.addAttribute(CharTermAttribute.class);
+        final PositionIncrementAttribute position = output.addAttribute(PositionIncrementAttribute.class);
+        int inputIndex = -1;
+        output.reset();
+        while (output.incrementToken()) {
+            if (position.getPositionIncrement() > 0) { inputIndex += position.getPositionIncrement(); }
+            if (inputIndex >= 0 && inputIndex < candidates.size()) { candidates.get(inputIndex).add(term.toString()); }
+        }
+        output.end();
+        output.close();
+        final String[] primary = firstTokenFilterOutputs(tokens, factory, null);
+        final List<List<String>> result = new java.util.ArrayList<>(tokens.length);
+        for (int index = 0; index < tokens.length; index++) {
+            candidates.get(index).add(primary[index]);
+            result.add(List.copyOf(candidates.get(index)));
+        }
+        return List.copyOf(result);
+    }
+
     /**
      * Adds Lucene lower-case normalization.
      *

src/test/java/org/egothor/stemmer/benchmark/PaiceHuskLancasterStemmerTest.java

@@ -58,10 +58,10 @@ final class PaiceHuskLancasterStemmerTest {
      */
     private static final String[][] SAMPLE_STEMS = {
             { "running", "run" },
-            { "caresses", "cares" },
+            { "caresses", "caress" },
-            { "happiness", "happi" },
+            { "happiness", "happy" },
             { "connected", "connect" },
-            { "dancing", "danc" },
+            { "dancing", "dant" },
             { "happy", "happy" }
     };
 
@@ -116,7 +116,7 @@ final class PaiceHuskLancasterStemmerTest {
         final Object stemmer = createStemmer();
         final Method stemMethod = stemMethod();
 
-        assertEquals("running", stemMethod.invoke(stemmer, "running"));
+        assertEquals("run", stemMethod.invoke(stemmer, "running"));
         assertEquals(null, stemMethod.invoke(stemmer, new Object[] { null }));
         assertNotNull(stemMethod.invoke(stemmer, "connected"));
     }

src/test/java/org/egothor/stemmer/benchmark/quality/BundledGoldStandardLoader.java

@@ -0,0 +1,58 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
+import java.nio.charset.StandardCharsets;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Objects;
+import java.util.zip.GZIPInputStream;
+
+import org.egothor.stemmer.CaseProcessingMode;
+import org.egothor.stemmer.StemmerDictionaryParser;
+import org.egothor.stemmer.StemmerPatchTrieLoader.Language;
+
+/** Loads gold-standard groups from authoritative bundled dictionary resources. */
+public final class BundledGoldStandardLoader {
+    /** Utility class. */
+    private BundledGoldStandardLoader() { throw new AssertionError("No instances."); }
+
+    /**
+     * Parses one compressed UTF-8 dictionary with case preserved.
+     * @param language registered bundled language
+     * @return immutable groups in source-row order
+     * @throws IOException if the resource is absent, malformed, or unreadable
+     */
+    public static List<GoldStandardGroup> load(final Language language) throws IOException {
+        Objects.requireNonNull(language, "language");
+        final String resource = language.resourcePath();
+        final List<GoldStandardGroup> groups = new ArrayList<>();
+        try (InputStream raw = openResource(language, resource); InputStream gzip = new GZIPInputStream(raw);
+                BufferedReader reader = new BufferedReader(new InputStreamReader(gzip, StandardCharsets.UTF_8))) {
+            StemmerDictionaryParser.parse(reader, resource, CaseProcessingMode.AS_IS, (stem, variants, row) -> {
+                final List<String> forms = new ArrayList<>(variants.length + 1);
+                forms.add(stem);
+                forms.addAll(Arrays.asList(variants));
+                try {
+                    groups.add(new GoldStandardGroup(row, forms));
+                } catch (IllegalArgumentException exception) {
+                    throw new IOException("Invalid dictionary group for language " + language + ", resource "
+                            + resource + ", row " + row + ": " + exception.getMessage(), exception);
+                }
+            });
+        }
+        return List.copyOf(groups);
+    }
+
+    /** Opens one required classpath resource with a precise language diagnostic. */
+    private static InputStream openResource(final Language language, final String resource) throws IOException {
+        final InputStream input = Thread.currentThread().getContextClassLoader().getResourceAsStream(resource);
+        if (input == null) {
+            throw new IOException("Dictionary resource is missing for language " + language + ": " + resource + ".");
+        }
+        return input;
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/CandidateAwareEvaluator.java

@@ -0,0 +1,165 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import java.util.TreeSet;
+
+import org.egothor.stemmer.benchmark.QualityStemmerMatrix.BatchStemmer;
+
+/**
+ * Calculates exact candidate-intersection pair metrics from canonical candidate-set signatures.
+ * Candidate-aware output defines an overlap relation rather than a partition. The algorithm
+ * aggregates signature frequencies and uses an inverted candidate index; it never enumerates
+ * complete dictionary word pairs. All pair arithmetic is checked.
+ */
+final class CandidateAwareEvaluator {
+    /** Utility class. */
+    private CandidateAwareEvaluator() { throw new AssertionError("No instances."); }
+
+    /** Evaluates one genuinely multi-output scenario through its authoritative JMH adapter. */
+    static QualityResult evaluate(final String stemmerName, final String language, final ProcessingMode mode,
+            final OutputPolicy policy, final List<GoldStandardGroup> groups, final BatchStemmer stemmer) throws IOException {
+        if (policy == OutputPolicy.PRIMARY_OUTPUT) {
+            throw new IllegalArgumentException("Candidate-aware evaluation requires ANY_CANDIDATE or ALL_CANDIDATES.");
+        }
+        final List<GoldStandardGroup> includedGroups = groups.stream().filter(group -> mode.includes(group.forms())).toList();
+        final List<String> forms = new ArrayList<>();
+        final List<Integer> groupIndexes = new ArrayList<>();
+        long singletonRows = 0;
+        long pairRows = 0;
+        long underPossible = 0;
+        for (int groupIndex = 0; groupIndex < includedGroups.size(); groupIndex++) {
+            final GoldStandardGroup group = includedGroups.get(groupIndex);
+            if (group.forms().size() == 1) { singletonRows = add(singletonRows, 1, "singleton rows"); }
+            else { pairRows = add(pairRows, 1, "rows contributing under-stemming pairs"); }
+            underPossible = add(underPossible, QualityEvaluator.chooseTwo(group.forms().size()), "under denominator");
+            for (String form : group.forms()) { forms.add(form); groupIndexes.add(groupIndex); }
+        }
+        final String[] input = forms.toArray(String[]::new);
+        final String[] primary = stemmer.stem(input);
+        final List<List<String>> rawCandidates = stemmer.stemCandidates(input);
+        if (primary == null || primary.length != input.length || rawCandidates == null || rawCandidates.size() != input.length) {
+            throw failure(stemmerName, language, mode, policy, "the adapter returned an invalid output batch");
+        }
+
+        final Map<Signature, SignatureCount> counts = new HashMap<>();
+        final Set<String> distinctCandidates = new HashSet<>();
+        long oneCandidate = 0;
+        long multipleCandidates = 0;
+        long maximumCandidates = 0;
+        long assignments = 0;
+        for (int index = 0; index < input.length; index++) {
+            final Signature signature = signature(rawCandidates.get(index), primary[index], stemmerName, language,
+                    mode, policy, includedGroups.get(groupIndexes.get(index)).rowNumber(), input[index]);
+            final int size = signature.candidates().size();
+            if (size == 1) { oneCandidate = add(oneCandidate, 1, "single-candidate forms"); }
+            else { multipleCandidates = add(multipleCandidates, 1, "multi-candidate forms"); }
+            maximumCandidates = Math.max(maximumCandidates, size);
+            assignments = add(assignments, size, "candidate assignments");
+            distinctCandidates.addAll(signature.candidates());
+            counts.computeIfAbsent(signature, ignored -> new SignatureCount()).increment(groupIndexes.get(index));
+        }
+
+        final List<Map.Entry<Signature, SignatureCount>> signatures = new ArrayList<>(counts.entrySet());
+        signatures.sort(Map.Entry.comparingByKey());
+        long sameGroupRelated = 0;
+        long crossGroupRelated = 0;
+        final Map<String, List<Integer>> inverted = new HashMap<>();
+        for (int index = 0; index < signatures.size(); index++) {
+            final Map.Entry<Signature, SignatureCount> entry = signatures.get(index);
+            long sameWithin = 0;
+            for (long groupCount : entry.getValue().byGroup().values()) {
+                sameWithin = add(sameWithin, QualityEvaluator.chooseTwo(groupCount), "same-signature group pairs");
+            }
+            sameGroupRelated = add(sameGroupRelated, sameWithin, "same-group related pairs");
+            if (policy == OutputPolicy.ALL_CANDIDATES || entry.getKey().candidates().size() == 1) {
+                crossGroupRelated = add(crossGroupRelated,
+                        subtract(QualityEvaluator.chooseTwo(entry.getValue().total()), sameWithin, "same-signature cross pairs"),
+                        "cross-group related pairs");
+            }
+            for (String candidate : entry.getKey().candidates()) {
+                inverted.computeIfAbsent(candidate, ignored -> new ArrayList<>()).add(index);
+            }
+        }
+        final Set<SignaturePair> relatedSignaturePairs = new HashSet<>();
+        for (List<Integer> indexes : inverted.values()) {
+            for (int left = 0; left < indexes.size(); left++) {
+                for (int right = left + 1; right < indexes.size(); right++) {
+                    relatedSignaturePairs.add(new SignaturePair(indexes.get(left), indexes.get(right)));
+                }
+            }
+        }
+        for (SignaturePair pair : relatedSignaturePairs) {
+            final SignatureCount left = signatures.get(pair.left()).getValue();
+            final SignatureCount right = signatures.get(pair.right()).getValue();
+            long same = 0;
+            for (Map.Entry<Integer, Long> group : left.byGroup().entrySet()) {
+                same = add(same, multiply(group.getValue(), right.byGroup().getOrDefault(group.getKey(), 0L),
+                        "different-signature same-group pairs"), "same-group related pairs");
+            }
+            final long total = multiply(left.total(), right.total(), "different-signature pairs");
+            sameGroupRelated = add(sameGroupRelated, same, "same-group related pairs");
+            if (policy == OutputPolicy.ALL_CANDIDATES) {
+                crossGroupRelated = add(crossGroupRelated, subtract(total, same, "different-signature cross pairs"),
+                        "cross-group related pairs");
+            }
+        }
+        final long wordCount = input.length;
+        final long overPossible = subtract(QualityEvaluator.chooseTwo(wordCount), underPossible, "over denominator");
+        final long underError = subtract(underPossible, sameGroupRelated, "candidate under errors");
+        return new QualityResult(stemmerName, language, mode, policy,
+                includedGroups.size(), wordCount, singletonRows, pairRows, oneCandidate, multipleCandidates,
+                maximumCandidates, assignments, distinctCandidates.size(), crossGroupRelated, overPossible,
+                underError, underPossible, null);
+    }
+
+    /** Canonicalizes and validates one adapter candidate collection. */
+    private static Signature signature(final List<String> raw, final String primary, final String stemmer,
+            final String language, final ProcessingMode mode, final OutputPolicy policy,
+            final int row, final String form) throws IOException {
+        if (primary == null) { throw failure(stemmer, language, mode, policy, "null primary output at row " + row + " for '" + form + "'"); }
+        if (raw == null || raw.isEmpty()) { throw failure(stemmer, language, mode, policy, "null or empty candidate collection at row " + row + " for '" + form + "'"); }
+        final TreeSet<String> candidates = new TreeSet<>();
+        for (String candidate : raw) {
+            if (candidate == null) { throw failure(stemmer, language, mode, policy, "null candidate at row " + row + " for '" + form + "'"); }
+            candidates.add(candidate);
+        }
+        if (!candidates.contains(primary)) { throw failure(stemmer, language, mode, policy, "candidate set omits primary output '" + primary + "' at row " + row + " for '" + form + "'"); }
+        return new Signature(List.copyOf(candidates));
+    }
+
+    /** Checked addition with diagnostic context. */
+    private static long add(final long left, final long right, final String context) { try { return Math.addExact(left, right); } catch (ArithmeticException exception) { throw new IllegalStateException("Arithmetic overflow in " + context + ".", exception); } }
+    /** Checked subtraction with diagnostic context. */
+    private static long subtract(final long left, final long right, final String context) { try { return Math.subtractExact(left, right); } catch (ArithmeticException exception) { throw new IllegalStateException("Arithmetic overflow in " + context + ".", exception); } }
+    /** Checked multiplication with diagnostic context. */
+    private static long multiply(final long left, final long right, final String context) { try { return Math.multiplyExact(left, right); } catch (ArithmeticException exception) { throw new IllegalStateException("Arithmetic overflow in " + context + ".", exception); } }
+    /** Creates one scenario-qualified adapter failure. */
+    private static IOException failure(final String stemmer, final String language, final ProcessingMode mode,
+            final OutputPolicy policy, final String reason) { return new IOException("Candidate-aware evaluation failed for stemmer " + stemmer + ", language " + language + ", dictionary mode " + mode + ", and output policy " + policy + ": " + reason + "."); }
+
+    /** Deterministic immutable candidate-set signature. */
+    private record Signature(List<String> candidates) implements Comparable<Signature> {
+        /** Orders signatures lexicographically without depending on map iteration. */
+        @Override public int compareTo(final Signature other) {
+            final int common = Math.min(candidates.size(), other.candidates.size());
+            for (int index = 0; index < common; index++) { final int compared = candidates.get(index).compareTo(other.candidates.get(index)); if (compared != 0) { return compared; } }
+            return Integer.compare(candidates.size(), other.candidates.size());
+        }
+    }
+    /** Aggregated global and per-group frequency of one signature. */
+    private static final class SignatureCount {
+        private long total;
+        private final Map<Integer, Long> byGroup = new HashMap<>();
+        /** Adds one word occurrence. */ private void increment(final int group) { total = add(total, 1, "signature frequency"); byGroup.merge(group, 1L, (left, right) -> add(left, right, "signature group frequency")); }
+        /** @return global signature frequency */ private long total() { return total; }
+        /** @return mutable internally owned per-group frequencies */ private Map<Integer, Long> byGroup() { return byGroup; }
+    }
+    /** Unordered pair of distinct canonical signature indexes. */
+    private record SignaturePair(int left, int right) { }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/CandidateAwareEvaluatorTest.java

@@ -0,0 +1,199 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.LinkedHashSet;
+import java.util.List;
+import java.util.Map;
+import java.util.Random;
+import java.util.Set;
+
+import org.egothor.stemmer.benchmark.QualityStemmerMatrix.BatchStemmer;
+import org.junit.jupiter.api.DisplayName;
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+
+/** Exact candidate-relation tests, including an independent quadratic oracle. */
+@Tag("unit")
+@DisplayName("Candidate-aware pairwise stemming quality")
+final class CandidateAwareEvaluatorTest {
+    /** Verifies intersections repair under-stemming while several shared candidates count once. */
+    @Test @DisplayName("Candidate intersections repair primary under-stemming and count each pair once")
+    void intersectionsRepairUnderStemming() throws IOException {
+        final List<GoldStandardGroup> groups = List.of(new GoldStandardGroup(1, List.of("a", "b", "c")));
+        final Map<String, String> primary = Map.of("a", "y", "b", "x", "c", "z");
+        final Map<String, List<String>> candidates = Map.of("a", List.of("y", "x", "x"),
+                "b", List.of("x", "shared"), "c", List.of("z", "x", "shared"));
+        final QualityResult primaryResult = QualityEvaluator.evaluateBatch("Synthetic", "MULTI",
+                ProcessingMode.ALL_WORDS, groups, adapter(primary, candidates));
+        final QualityResult candidateResult = CandidateAwareEvaluator.evaluate("Synthetic", "MULTI",
+                ProcessingMode.ALL_WORDS, OutputPolicy.ALL_CANDIDATES, groups, adapter(primary, candidates));
+        assertEquals(3, primaryResult.underErrorPairs());
+        assertEquals(0, candidateResult.underErrorPairs());
+        assertEquals(7, candidateResult.totalCandidateAssignments(), "Duplicate candidates must be removed per word.");
+        assertTrue(candidateResult.underErrorPairs() <= primaryResult.underErrorPairs());
+    }
+
+    /** Verifies exact within-row disconnections and cross-row candidate collisions. */
+    @Test @DisplayName("Disjoint sets and cross-group intersections produce exact candidate-aware counts")
+    void disjointAndCollidingSets() throws IOException {
+        final List<GoldStandardGroup> groups = List.of(
+                new GoldStandardGroup(1, List.of("a", "b")), new GoldStandardGroup(2, List.of("c", "d")));
+        final Map<String, String> primary = Map.of("a", "a", "b", "b", "c", "c", "d", "d");
+        final Map<String, List<String>> candidates = Map.of("a", List.of("a", "collision"), "b", List.of("b"),
+                "c", List.of("c", "collision", "other"), "d", List.of("d", "other"));
+        final QualityResult result = CandidateAwareEvaluator.evaluate("Synthetic", "MULTI",
+                ProcessingMode.ALL_WORDS, OutputPolicy.ALL_CANDIDATES, groups, adapter(primary, candidates));
+        assertEquals(1, result.underErrorPairs());
+        assertEquals(2, result.underPossiblePairs());
+        assertEquals(1, result.overErrorPairs(), "Only the cross-group a-c pair shares a candidate.");
+        assertEquals(4, result.overPossiblePairs());
+    }
+
+    /** Verifies optimistic and all-active cross-group semantics for canonical examples. */
+    @Test @DisplayName("ANY_CANDIDATE and ALL_CANDIDATES apply their distinct over-stemming relations")
+    void policySpecificOverStemming() throws IOException {
+        assertPolicyOver(List.of("x"), List.of("x"), 1, 1);
+        assertPolicyOver(List.of("x"), List.of("y"), 0, 0);
+        assertPolicyOver(List.of("x"), List.of("x", "y"), 0, 1);
+        assertPolicyOver(List.of("x", "y"), List.of("x", "y"), 0, 1);
+        assertPolicyOver(List.of("x", "y"), List.of("x", "z"), 0, 1);
+    }
+
+    /** Verifies both candidate policies have identical same-group under-stemming. */
+    @Test @DisplayName("Candidate policies share the exact same within-group intersection rule")
+    void candidatePoliciesShareUnderStemming() throws IOException {
+        final List<GoldStandardGroup> groups = List.of(new GoldStandardGroup(1, List.of("a", "b", "c")));
+        final Map<String, String> primary = Map.of("a", "x", "b", "y", "c", "z");
+        final Map<String, List<String>> candidates = Map.of("a", List.of("x", "shared"),
+                "b", List.of("y", "shared"), "c", List.of("z"));
+        final QualityResult any = CandidateAwareEvaluator.evaluate("Synthetic", "MULTI", ProcessingMode.ALL_WORDS,
+                OutputPolicy.ANY_CANDIDATE, groups, adapter(primary, candidates));
+        final QualityResult all = CandidateAwareEvaluator.evaluate("Synthetic", "MULTI", ProcessingMode.ALL_WORDS,
+                OutputPolicy.ALL_CANDIDATES, groups, adapter(primary, candidates));
+        assertEquals(2, any.underErrorPairs()); assertEquals(any.underErrorPairs(), all.underErrorPairs());
+    }
+
+    /** Compares the optimized signature algorithm with an independent fixed-seed oracle. */
+    @Test @DisplayName("Optimized candidate metrics equal a deterministic randomized brute-force oracle")
+    void randomizedOracleAgreement() throws IOException {
+        final Random random = new Random(0x5EEDC0DEL);
+        for (int trial = 0; trial < 150; trial++) {
+            final int groupCount = 1 + random.nextInt(5);
+            final List<GoldStandardGroup> groups = new ArrayList<>();
+            final Map<String, String> primary = new HashMap<>();
+            final Map<String, List<String>> candidates = new HashMap<>();
+            int word = 0;
+            for (int group = 0; group < groupCount; group++) {
+                final List<String> forms = new ArrayList<>();
+                for (int member = 0; member < 1 + random.nextInt(5); member++) {
+                    final String form = "w" + word++;
+                    forms.add(form);
+                    final String primaryStem = "s" + random.nextInt(7);
+                    primary.put(form, primaryStem);
+                    final List<String> raw = new ArrayList<>();
+                    raw.add(primaryStem);
+                    for (int candidate = 0; candidate < random.nextInt(4); candidate++) {
+                        raw.add("s" + random.nextInt(7));
+                    }
+                    candidates.put(form, raw);
+                }
+                groups.add(new GoldStandardGroup(group + 1, forms));
+            }
+            final QualityResult optimized = CandidateAwareEvaluator.evaluate("Random", "MULTI",
+                    ProcessingMode.ALL_WORDS, OutputPolicy.ALL_CANDIDATES, groups, adapter(primary, candidates));
+            final QualityResult any = CandidateAwareEvaluator.evaluate("Random", "MULTI",
+                    ProcessingMode.ALL_WORDS, OutputPolicy.ANY_CANDIDATE, groups, adapter(primary, candidates));
+            final QualityResult primaryResult = QualityEvaluator.evaluateBatch("Random", "MULTI",
+                    ProcessingMode.ALL_WORDS, groups, adapter(primary, candidates));
+            final long[] oracle = oracle(groups, candidates);
+            assertEquals(oracle[0], optimized.underErrorPairs(), "Under errors differ in trial " + trial);
+            assertEquals(oracle[1], optimized.underPossiblePairs(), "Under denominator differs in trial " + trial);
+            assertEquals(oracle[2], optimized.overErrorPairs(), "Over errors differ in trial " + trial);
+            assertEquals(oracle[3], optimized.overPossiblePairs(), "Over denominator differs in trial " + trial);
+            assertEquals(oracle[4], any.overErrorPairs(), "Optimistic over errors differ in trial " + trial);
+            assertEquals(any.underErrorPairs(), optimized.underErrorPairs());
+            assertTrue(any.underErrorPairs() <= primaryResult.underErrorPairs());
+            assertTrue(any.overErrorPairs() <= primaryResult.overErrorPairs());
+            assertTrue(optimized.overErrorPairs() >= primaryResult.overErrorPairs());
+        }
+    }
+
+    /** Verifies candidate contract violations fail with scenario and word context. */
+    @Test @DisplayName("Invalid candidate collections fail with precise contextual diagnostics")
+    void invalidCandidateOutput() {
+        final List<GoldStandardGroup> groups = List.of(new GoldStandardGroup(7, List.of("žluťoučký")));
+        final BatchStemmer invalid = adapter(Map.of("žluťoučký", "stem"), Map.of("žluťoučký", List.of("other")));
+        final IOException exception = assertThrows(IOException.class, () -> CandidateAwareEvaluator.evaluate(
+                "Invalid", "CS_CZ", ProcessingMode.ALL_WORDS, OutputPolicy.ALL_CANDIDATES, groups, invalid));
+        assertTrue(exception.getMessage().contains("row 7"));
+        assertTrue(exception.getMessage().contains("žluťoučký"));
+        assertTrue(exception.getMessage().contains("omits primary output"));
+    }
+
+    /** Creates a deterministic multi-output adapter from per-form fixtures. */
+    private static BatchStemmer adapter(final Map<String, String> primary,
+            final Map<String, List<String>> candidates) {
+        return new BatchStemmer() {
+            /** {@inheritDoc} */
+            @Override public String[] stem(final String[] forms) {
+                final String[] outputs = new String[forms.length];
+                for (int index = 0; index < forms.length; index++) { outputs[index] = primary.get(forms[index]); }
+                return outputs;
+            }
+            /** {@inheritDoc} */
+            @Override public List<List<String>> stemCandidates(final String[] forms) {
+                final List<List<String>> outputs = new ArrayList<>();
+                for (String form : forms) { outputs.add(candidates.get(form)); }
+                return outputs;
+            }
+            /** {@inheritDoc} */
+            @Override public boolean supportsMultipleOutputs() { return true; }
+        };
+    }
+
+    /** Evaluates one two-row example and checks both policy numerators. */
+    private static void assertPolicyOver(final List<String> left, final List<String> right,
+            final long expectedAny, final long expectedAll) throws IOException {
+        final List<GoldStandardGroup> groups = List.of(new GoldStandardGroup(1, List.of("a")),
+                new GoldStandardGroup(2, List.of("b")));
+        final Map<String, String> primary = Map.of("a", left.get(0), "b", right.get(0));
+        final Map<String, List<String>> candidates = Map.of("a", left, "b", right);
+        final QualityResult any = CandidateAwareEvaluator.evaluate("Synthetic", "MULTI", ProcessingMode.ALL_WORDS,
+                OutputPolicy.ANY_CANDIDATE, groups, adapter(primary, candidates));
+        final QualityResult all = CandidateAwareEvaluator.evaluate("Synthetic", "MULTI", ProcessingMode.ALL_WORDS,
+                OutputPolicy.ALL_CANDIDATES, groups, adapter(primary, candidates));
+        assertEquals(expectedAny, any.overErrorPairs()); assertEquals(expectedAll, all.overErrorPairs());
+    }
+
+    /** Enumerates small word pairs independently and returns under error/possible and over error/possible counts. */
+    private static long[] oracle(final List<GoldStandardGroup> groups,
+            final Map<String, List<String>> candidates) {
+        final List<String> forms = new ArrayList<>();
+        final List<Integer> labels = new ArrayList<>();
+        for (int group = 0; group < groups.size(); group++) {
+            for (String form : groups.get(group).forms()) { forms.add(form); labels.add(group); }
+        }
+        long underError = 0; long underPossible = 0; long overError = 0; long overPossible = 0; long anyOverError = 0;
+        for (int left = 0; left < forms.size(); left++) {
+            for (int right = left + 1; right < forms.size(); right++) {
+                final Set<String> intersection = new LinkedHashSet<>(candidates.get(forms.get(left)));
+                intersection.retainAll(new LinkedHashSet<>(candidates.get(forms.get(right))));
+                if (labels.get(left).equals(labels.get(right))) {
+                    underPossible++; if (intersection.isEmpty()) { underError++; }
+                } else {
+                    overPossible++; if (!intersection.isEmpty()) { overError++; }
+                    final Set<String> leftSet = new LinkedHashSet<>(candidates.get(forms.get(left)));
+                    final Set<String> rightSet = new LinkedHashSet<>(candidates.get(forms.get(right)));
+                    if (leftSet.size() == 1 && leftSet.equals(rightSet)) { anyOverError++; }
+                }
+            }
+        }
+        return new long[] {underError, underPossible, overError, overPossible, anyOverError};
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/CandidateQualityAudit.java

@@ -0,0 +1,121 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import java.io.IOException;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.nio.file.StandardOpenOption;
+import java.util.ArrayList;
+import java.util.Comparator;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import java.util.TreeSet;
+
+import org.egothor.stemmer.benchmark.QualityStemmerMatrix.BatchStemmer;
+import org.egothor.stemmer.benchmark.QualityStemmerMatrix.Candidate;
+
+/** Produces deterministic word-level diagnostics for genuinely multi-output adapters. */
+final class CandidateQualityAudit {
+    /** Utility class. */
+    private CandidateQualityAudit() { throw new AssertionError("No instances."); }
+
+    /** Evaluates candidate output and retains the largest candidate sets for reproducible inspection. */
+    static Scenario evaluate(final Candidate candidate, final ProcessingMode mode,
+            final List<GoldStandardGroup> groups, final QualityResult primary, final QualityResult any,
+            final int limit) throws IOException {
+        final List<String> forms = new ArrayList<>();
+        final List<Integer> groupIndexes = new ArrayList<>();
+        final List<Integer> rows = new ArrayList<>();
+        for (int group = 0; group < groups.size(); group++) {
+            final GoldStandardGroup item = groups.get(group);
+            if (!mode.includes(item.forms())) { continue; }
+            for (String form : item.forms()) { forms.add(form); groupIndexes.add(group); rows.add(item.rowNumber()); }
+        }
+        final BatchStemmer stemmer = candidate.createStemmer();
+        final String[] primaryOutputs = stemmer.stem(forms.toArray(String[]::new));
+        final List<List<String>> rawCandidates = stemmer.stemCandidates(forms.toArray(String[]::new));
+        final Map<String, List<Integer>> inverted = new HashMap<>();
+        final Map<Integer, Long> candidateCountDistribution = new java.util.TreeMap<>();
+        final List<List<String>> candidateSets = new ArrayList<>();
+        for (int index = 0; index < forms.size(); index++) {
+            final TreeSet<String> canonical = new TreeSet<>(rawCandidates.get(index));
+            canonical.add(primaryOutputs[index]);
+            final List<String> set = List.copyOf(canonical);
+            candidateSets.add(set);
+            candidateCountDistribution.merge(set.size(), 1L, Math::addExact);
+            for (String value : set) { inverted.computeIfAbsent(value, ignored -> new ArrayList<>()).add(index); }
+        }
+        final QualityResult candidateResult = CandidateAwareEvaluator.evaluate(candidate.name(), candidate.language().name(),
+                mode, OutputPolicy.ALL_CANDIDATES, groups, candidate.createStemmer());
+        final List<Integer> selected = new ArrayList<>();
+        for (int index = 0; index < forms.size(); index++) { if (candidateSets.get(index).size() > 1) { selected.add(index); } }
+        selected.sort(Comparator.<Integer>comparingInt(index -> candidateSets.get(index).size()).reversed()
+                .thenComparing(index -> forms.get(index)).thenComparingInt(index -> rows.get(index)));
+        final List<Word> words = new ArrayList<>();
+        for (int index : selected.subList(0, Math.min(limit, selected.size()))) {
+            final Set<Integer> partners = new HashSet<>();
+            for (String value : candidateSets.get(index)) { partners.addAll(inverted.get(value)); }
+            partners.remove(index);
+            long repaired = 0; long introduced = 0;
+            for (int partner : partners) {
+                final boolean primaryRelated = primaryOutputs[index].equals(primaryOutputs[partner]);
+                if (groupIndexes.get(index).equals(groupIndexes.get(partner)) && !primaryRelated) { repaired++; }
+                if (!groupIndexes.get(index).equals(groupIndexes.get(partner)) && !primaryRelated) { introduced++; }
+            }
+            words.add(new Word(rows.get(index), forms.get(index), primaryOutputs[index], candidateSets.get(index),
+                    repaired, introduced));
+        }
+        return new Scenario(primary, any, candidateResult, Map.copyOf(candidateCountDistribution), List.copyOf(words));
+    }
+
+    /** Appends candidate diagnostics to the freshly generated audit report. */
+    static void append(final Path path, final List<Scenario> scenarios) throws IOException {
+        if (scenarios.isEmpty()) { return; }
+        final StringBuilder text = new StringBuilder(4096);
+        text.append("\n# Candidate-aware audit\n\nWord-level sections below retain original Unicode forms. Per-word repaired and introduced counts describe relations involving that word and are diagnostic, not additive scenario totals.\n\n");
+        for (Scenario scenario : scenarios.stream().sorted(Comparator.comparing(Scenario::candidate, QualityResult.ORDER)).toList()) {
+            final QualityResult primary = scenario.primary();
+            final QualityResult any = scenario.any();
+            final QualityResult candidate = scenario.candidate();
+            text.append("## ").append(candidate.stemmer()).append(" / ").append(candidate.language()).append(" / ")
+                    .append(candidate.processingMode()).append(" / ALL_CANDIDATES\n\n")
+                    .append("- Primary under-stemming pairs: ").append(primary.underErrorPairs()).append(" / ").append(primary.underPossiblePairs()).append("\n")
+                    .append("- ANY_CANDIDATE under-stemming pairs: ").append(any.underErrorPairs()).append(" / ").append(any.underPossiblePairs()).append("\n")
+                    .append("- ALL_CANDIDATES under-stemming pairs: ").append(candidate.underErrorPairs()).append(" / ").append(candidate.underPossiblePairs()).append("\n")
+                    .append("- Under-stemming pairs repaired by alternatives: ").append(primary.underErrorPairs() - candidate.underErrorPairs()).append("\n")
+                    .append("- Primary over-stemming pairs: ").append(primary.overErrorPairs()).append(" / ").append(primary.overPossiblePairs()).append("\n")
+                    .append("- ANY_CANDIDATE over-stemming pairs: ").append(any.overErrorPairs()).append(" / ").append(any.overPossiblePairs()).append("\n")
+                    .append("- Best-case over-stemming pairs avoided: ").append(primary.overErrorPairs() - any.overErrorPairs()).append("\n")
+                    .append("- ALL_CANDIDATES over-stemming pairs: ").append(candidate.overErrorPairs()).append(" / ").append(candidate.overPossiblePairs()).append("\n")
+                    .append("- Additional candidate collision pairs: ").append(candidate.overErrorPairs() - primary.overErrorPairs()).append("\n")
+                    .append("- Forms with multiple candidates: ").append(candidate.formsWithMultipleCandidates()).append("\n")
+                    .append("- Maximum candidates for one word: ").append(candidate.maximumCandidatesForOneWord()).append("\n\n")
+                    .append("- Candidate-count distribution: ").append(new java.util.TreeMap<>(scenario.candidateCountDistribution())).append("\n\n")
+                    .append("### Forms with the largest candidate sets\n\n");
+            for (Word word : scenario.words()) {
+                text.append("- Row ").append(word.row()).append(", form `").append(escape(word.form()))
+                        .append("`, primary `").append(escape(word.primary())).append("`, candidates ")
+                        .append(word.candidates().stream().map(value -> "`" + escape(value) + "`").toList())
+                        .append(", repaired same-group relations ").append(word.repairedUnderRelations())
+                        .append(", introduced cross-group relations ").append(word.introducedOverRelations()).append(".\n");
+            }
+            text.append('\n');
+        }
+        Files.writeString(path, text.toString(), StandardCharsets.UTF_8, StandardOpenOption.APPEND);
+    }
+
+    /** Escapes Markdown code-span delimiters without altering linguistic content. */
+    private static String escape(final String value) { return value.replace("`", "\\`"); }
+
+    /** Immutable candidate-aware audit scenario. */
+    record Scenario(QualityResult primary, QualityResult any, QualityResult candidate,
+            Map<Integer, Long> candidateCountDistribution,
+            List<Word> words) { }
+
+    /** Immutable word-level candidate diagnostic. */
+    record Word(int row, String form, String primary, List<String> candidates,
+            long repairedUnderRelations, long introducedOverRelations) { }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/GoldStandardGroup.java

@@ -0,0 +1,35 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import java.util.LinkedHashSet;
+import java.util.List;
+import java.util.Objects;
+import java.util.Set;
+
+/** Immutable gold-standard equivalence class originating from one dictionary row. */
+public record GoldStandardGroup(int rowNumber, List<String> forms) {
+    private static final int FIRST_ROW_NUMBER = 1;
+    /**
+     * Creates a group while removing exact duplicates within this row.
+     *
+     * @param rowNumber positive physical dictionary row number
+     * @param forms supplied forms; encounter order has no metric significance
+     * @throws IllegalArgumentException if the row or forms are invalid
+     */
+    public GoldStandardGroup {
+        if (rowNumber < FIRST_ROW_NUMBER) {
+            throw new IllegalArgumentException("Dictionary row number must be positive.");
+        }
+        Objects.requireNonNull(forms, "forms");
+        final Set<String> distinct = new LinkedHashSet<>();
+        for (String form : forms) {
+            if (form == null || form.isEmpty()) {
+                throw new IllegalArgumentException("Dictionary group forms must be non-empty strings.");
+            }
+            distinct.add(form);
+        }
+        if (distinct.isEmpty()) {
+            throw new IllegalArgumentException("A dictionary group must contain at least one usable form.");
+        }
+        forms = List.copyOf(distinct);
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/LanguageUniverse.java

@@ -0,0 +1,53 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.ArrayList;
+import java.util.EnumMap;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import java.util.TreeSet;
+
+import org.egothor.stemmer.StemmerPatchTrieLoader.Language;
+
+/** Reconciles bundled dictionary resources with every production language enumeration value. */
+record LanguageUniverse(Map<Language, Path> dictionaries, List<String> resourceDirectories,
+        List<String> enumerationValues) {
+    /** Discovers and validates a one-to-one resource mapping without silent exclusions. */
+    static LanguageUniverse discover(final Path resourcesDirectory) throws IOException {
+        final Map<String, Path> resources = new HashMap<>();
+        try (java.util.stream.Stream<Path> paths = Files.list(resourcesDirectory)) {
+            for (Path directory : paths.filter(Files::isDirectory).toList()) {
+                final Path dictionary = directory.resolve("stemmer.gz");
+                if (Files.isRegularFile(dictionary)) {
+                    final Path previous = resources.put(directory.getFileName().toString(), dictionary);
+                    if (previous != null) { throw new IOException("Two dictionary resources map to directory " + directory + "."); }
+                }
+            }
+        }
+        final Map<Language, Path> mappings = new EnumMap<>(Language.class);
+        final Set<String> mappedDirectories = new TreeSet<>();
+        for (Language language : Language.values()) {
+            final Path dictionary = resources.get(language.resourceDirectory());
+            if (dictionary == null) {
+                throw new IOException("Enumeration language " + language + " has no stemmer.gz dictionary under "
+                        + resourcesDirectory + ".");
+            }
+            mappings.put(language, dictionary);
+            mappedDirectories.add(language.resourceDirectory());
+        }
+        final Set<String> unmatched = new TreeSet<>(resources.keySet());
+        unmatched.removeAll(mappedDirectories);
+        if (!unmatched.isEmpty()) {
+            throw new IOException("Dictionary resource directories have no StemmerPatchTrieLoader.Language mapping: "
+                    + unmatched + ".");
+        }
+        final List<String> enumValues = new ArrayList<>();
+        for (Language language : Language.values()) { enumValues.add(language.name()); }
+        return new LanguageUniverse(Map.copyOf(mappings), List.copyOf(new TreeSet<>(resources.keySet())),
+                List.copyOf(enumValues));
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/LanguageUniverseTest.java

@@ -0,0 +1,55 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.Path;
+
+import org.egothor.stemmer.StemmerPatchTrieLoader.Language;
+import org.junit.jupiter.api.DisplayName;
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+
+/** Regression tests for independent dictionary-resource and enumeration reconciliation. */
+@Tag("integration")
+@DisplayName("Authoritative Radixor language universe")
+final class LanguageUniverseTest {
+    /** Temporary resource tree. */ @TempDir Path temporaryDirectory;
+
+    /** Verifies every production enumeration value has exactly one bundled dictionary. */
+    @Test @DisplayName("Production resources reconcile with every language enumeration value")
+    void productionResourcesReconcile() throws IOException {
+        final LanguageUniverse universe = LanguageUniverse.discover(Path.of("src/main/resources"));
+        assertEquals(Language.values().length, universe.dictionaries().size());
+        assertTrue(universe.dictionaries().containsKey(Language.DA_DK));
+        assertTrue(universe.dictionaries().containsKey(Language.YI));
+    }
+
+    /** Verifies a missing enumerated resource produces an exact diagnostic. */
+    @Test @DisplayName("Missing enumeration resources fail validation")
+    void missingResourceFails() throws IOException {
+        final Path first = this.temporaryDirectory.resolve(Language.CS_CZ.resourceDirectory());
+        Files.createDirectories(first); Files.createFile(first.resolve("stemmer.gz"));
+        final IOException exception = assertThrows(IOException.class,
+                () -> LanguageUniverse.discover(this.temporaryDirectory));
+        assertTrue(exception.getMessage().contains("DA_DK"));
+    }
+
+    /** Verifies an unenumerated dictionary directory is rejected. */
+    @Test @DisplayName("Unmapped dictionary directories fail validation")
+    void extraResourceFails() throws IOException {
+        for (Language language : Language.values()) {
+            final Path directory = this.temporaryDirectory.resolve(language.resourceDirectory());
+            Files.createDirectories(directory); Files.createFile(directory.resolve("stemmer.gz"));
+        }
+        final Path extra = this.temporaryDirectory.resolve("unmapped_language");
+        Files.createDirectories(extra); Files.createFile(extra.resolve("stemmer.gz"));
+        final IOException exception = assertThrows(IOException.class,
+                () -> LanguageUniverse.discover(this.temporaryDirectory));
+        assertTrue(exception.getMessage().contains("unmapped_language"));
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/MetricCorrelationWriter.java

@@ -0,0 +1,108 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import java.io.IOException;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.ArrayList;
+import java.util.Comparator;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.OptionalDouble;
+import java.util.function.Function;
+
+/** Writes deterministic Pearson and tied-rank Spearman correlations within compatible cohorts. */
+final class MetricCorrelationWriter {
+    /** Stable metric extractors used for correlation analysis. */
+    private static final Map<String, Function<QualityResult, OptionalDouble>> METRICS = metrics();
+    /** Utility class. */
+    private MetricCorrelationWriter() { throw new AssertionError("No instances."); }
+
+    /** Writes both correlation reports from unrounded per-language scenario values. */
+    static void write(final Path pearson, final Path spearman, final List<QualityResult> results) throws IOException {
+        writeOne(pearson, results, false); writeOne(spearman, results, true);
+    }
+
+    /** Writes one correlation method with explicit missing-value reasons. */
+    private static void writeOne(final Path path, final List<QualityResult> results, final boolean ranks) throws IOException {
+        final StringBuilder output = new StringBuilder("Aggregation,Dictionary mode,Output policy,Metric A,Metric B,Observation count,Correlation,Missing-value reason\n");
+        for (ProcessingMode mode : ProcessingMode.values()) {
+            for (OutputPolicy policy : OutputPolicy.values()) {
+                final List<QualityResult> cohort = results.stream().filter(row -> row.processingMode() == mode
+                        && row.outputPolicy() == policy).toList();
+                final List<String> names = new ArrayList<>(METRICS.keySet());
+                if (policy != OutputPolicy.PRIMARY_OUTPUT) { names.remove("Adjusted Rand Index"); }
+                for (int left = 0; left < names.size(); left++) {
+                    for (int right = left; right < names.size(); right++) {
+                        append(output, mode, policy, names.get(left), names.get(right), cohort, ranks);
+                    }
+                }
+            }
+        }
+        final Path parent = path.toAbsolutePath().getParent(); if (parent != null) { Files.createDirectories(parent); }
+        Files.writeString(path, output.toString(), StandardCharsets.UTF_8);
+    }
+
+    /** Appends one coefficient after pairwise removal of undefined observations. */
+    private static void append(final StringBuilder output, final ProcessingMode mode, final OutputPolicy policy,
+            final String leftName, final String rightName, final List<QualityResult> cohort, final boolean ranks) {
+        final List<Double> left = new ArrayList<>(); final List<Double> right = new ArrayList<>();
+        for (QualityResult row : cohort) {
+            final OptionalDouble a = METRICS.get(leftName).apply(row); final OptionalDouble b = METRICS.get(rightName).apply(row);
+            if (a.isPresent() && b.isPresent()) { left.add(a.getAsDouble()); right.add(b.getAsDouble()); }
+        }
+        String value = ""; String reason = "";
+        if (left.size() < 3) { reason = "Fewer than three defined observations."; }
+        else {
+            final double[] a = ranks ? ranks(left) : values(left); final double[] b = ranks ? ranks(right) : values(right);
+            final OptionalDouble correlation = pearson(a, b);
+            if (correlation.isEmpty()) { reason = "At least one metric has zero variance."; }
+            else { value = String.format(java.util.Locale.ROOT, "%.12f", correlation.getAsDouble()); }
+        }
+        output.append("Per-language scenario,").append(mode).append(',').append(policy).append(',')
+                .append(csv(leftName)).append(',').append(csv(rightName)).append(',').append(left.size()).append(',')
+                .append(value).append(',').append(csv(reason)).append('\n');
+    }
+
+    /** Calculates Pearson correlation with an empty result for zero variance. */
+    private static OptionalDouble pearson(final double[] left, final double[] right) {
+        double leftMean = 0.0; double rightMean = 0.0;
+        for (int index = 0; index < left.length; index++) { leftMean += left[index]; rightMean += right[index]; }
+        leftMean /= left.length; rightMean /= right.length;
+        double covariance = 0.0; double leftVariance = 0.0; double rightVariance = 0.0;
+        for (int index = 0; index < left.length; index++) {
+            final double a = left[index] - leftMean; final double b = right[index] - rightMean;
+            covariance += a * b; leftVariance += a * a; rightVariance += b * b;
+        }
+        return leftVariance == 0.0 || rightVariance == 0.0 ? OptionalDouble.empty()
+                : OptionalDouble.of(covariance / Math.sqrt(leftVariance * rightVariance));
+    }
+
+    /** Assigns deterministic average ranks to tied values. */
+    private static double[] ranks(final List<Double> input) {
+        final List<Integer> order = new ArrayList<>(); for (int index = 0; index < input.size(); index++) { order.add(index); }
+        order.sort(Comparator.comparingDouble(input::get)); final double[] ranks = new double[input.size()];
+        int start = 0; while (start < order.size()) {
+            int end = start + 1; while (end < order.size() && input.get(order.get(start)).equals(input.get(order.get(end)))) { end++; }
+            final double rank = (start + 1 + end) / 2.0; for (int index = start; index < end; index++) { ranks[order.get(index)] = rank; }
+            start = end;
+        }
+        return ranks;
+    }
+    /** Copies boxed values into a primitive array. */
+    private static double[] values(final List<Double> values) { final double[] result = new double[values.size()]; for (int index = 0; index < result.length; index++) { result[index] = values.get(index); } return result; }
+    /** Defines stable metric names and unrounded extractors. */
+    private static Map<String, Function<QualityResult, OptionalDouble>> metrics() {
+        final Map<String, Function<QualityResult, OptionalDouble>> values = new LinkedHashMap<>();
+        values.put("Pairwise F0.5", row -> row.pairwiseMetrics().f05()); values.put("Pairwise F1", row -> row.pairwiseMetrics().f1());
+        values.put("Pairwise F2", row -> row.pairwiseMetrics().f2()); values.put("Jaccard", row -> row.pairwiseMetrics().jaccard());
+        values.put("Fowlkes-Mallows", row -> row.pairwiseMetrics().fowlkesMallows());
+        values.put("Matthews correlation coefficient", row -> row.pairwiseMetrics().matthewsCorrelationCoefficient());
+        values.put("Balanced accuracy", row -> row.pairwiseMetrics().balancedAccuracy());
+        values.put("Adjusted Rand Index", row -> row.partitionMetrics() == null ? OptionalDouble.empty() : OptionalDouble.of(row.partitionMetrics().adjustedRandIndex()));
+        return java.util.Collections.unmodifiableMap(values);
+    }
+    /** Quotes one CSV field. */
+    private static String csv(final String value) { return '"' + value.replace("\"", "\"\"") + '"'; }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/OutputPolicy.java

@@ -0,0 +1,11 @@
+package org.egothor.stemmer.benchmark.quality;
+
+/** Defines which outputs of a JMH stemmer adapter establish the measured relation. */
+enum OutputPolicy {
+    /** Uses only the deterministic output selected by the existing JMH comparison. */
+    PRIMARY_OUTPUT,
+    /** Uses an optimistic pair-specific choice from the complete candidate sets. */
+    ANY_CANDIDATE,
+    /** Treats all candidates as active and uses the complete intersection relation. */
+    ALL_CANDIDATES
+}

src/test/java/org/egothor/stemmer/benchmark/quality/PairwiseMetrics.java

@@ -0,0 +1,68 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import java.util.OptionalDouble;
+
+/**
+ * Derives scientifically labelled pairwise confusion metrics from unrounded raw counts.
+ * Undefined ratios are represented by empty optionals; no method returns NaN or infinity.
+ */
+record PairwiseMetrics(long truePositivePairs, long falsePositivePairs, long falseNegativePairs,
+        long trueNegativePairs) {
+    /** Creates checked confusion counts from one quality result. */
+    static PairwiseMetrics from(final QualityResult result) {
+        return new PairwiseMetrics(Math.subtractExact(result.underPossiblePairs(), result.underErrorPairs()),
+                result.overErrorPairs(), result.underErrorPairs(),
+                Math.subtractExact(result.overPossiblePairs(), result.overErrorPairs()));
+    }
+
+    /** @return pairwise precision */ OptionalDouble precision() { return ratio(truePositivePairs, Math.addExact(truePositivePairs, falsePositivePairs)); }
+    /** @return pairwise recall */ OptionalDouble recall() { return ratio(truePositivePairs, Math.addExact(truePositivePairs, falseNegativePairs)); }
+    /** @return pairwise specificity */ OptionalDouble specificity() { return ratio(trueNegativePairs, Math.addExact(trueNegativePairs, falsePositivePairs)); }
+    /** @return pairwise accuracy, potentially dominated by true negatives */
+    OptionalDouble accuracy() { return ratio(Math.addExact(truePositivePairs, trueNegativePairs), total()); }
+    /** @return arithmetic mean of recall and specificity */
+    OptionalDouble balancedAccuracy() { return mean(recall(), specificity()); }
+    /** @return pairwise F0.5 */ OptionalDouble f05() { return fBeta(0.25); }
+    /** @return pairwise F1 */ OptionalDouble f1() { return fBeta(1.0); }
+    /** @return pairwise F2 */ OptionalDouble f2() { return fBeta(4.0); }
+    /** @return Jaccard index */
+    OptionalDouble jaccard() { return ratio(truePositivePairs, Math.addExact(Math.addExact(truePositivePairs, falsePositivePairs), falseNegativePairs)); }
+    /** @return Fowlkes-Mallows index */
+    OptionalDouble fowlkesMallows() {
+        final OptionalDouble precisionValue = precision(); final OptionalDouble recallValue = recall();
+        return precisionValue.isEmpty() || recallValue.isEmpty() ? OptionalDouble.empty()
+                : OptionalDouble.of(Math.sqrt(precisionValue.getAsDouble() * recallValue.getAsDouble()));
+    }
+    /** @return Matthews correlation coefficient using scaled double arithmetic */
+    OptionalDouble matthewsCorrelationCoefficient() {
+        final double a = (double) truePositivePairs + falsePositivePairs;
+        final double b = (double) truePositivePairs + falseNegativePairs;
+        final double c = (double) trueNegativePairs + falsePositivePairs;
+        final double d = (double) trueNegativePairs + falseNegativePairs;
+        final double denominator = Math.sqrt(a * b * c * d);
+        if (denominator == 0.0) { return OptionalDouble.empty(); }
+        final double numerator = (double) truePositivePairs * trueNegativePairs
+                - (double) falsePositivePairs * falseNegativePairs;
+        return OptionalDouble.of(numerator / denominator);
+    }
+    /** @return pairwise error rate */
+    OptionalDouble errorRate() { return ratio(Math.addExact(falsePositivePairs, falseNegativePairs), total()); }
+
+    /** Calculates F-beta directly from raw counts. */
+    private OptionalDouble fBeta(final double betaSquared) {
+        final double numerator = (1.0 + betaSquared) * truePositivePairs;
+        final double denominator = numerator + betaSquared * falseNegativePairs + falsePositivePairs;
+        return denominator == 0.0 ? OptionalDouble.empty() : OptionalDouble.of(numerator / denominator);
+    }
+    /** Returns the checked total pair population. */
+    private long total() { return Math.addExact(Math.addExact(truePositivePairs, falsePositivePairs), Math.addExact(falseNegativePairs, trueNegativePairs)); }
+    /** Calculates one ratio with explicit zero-denominator handling. */
+    private static OptionalDouble ratio(final long numerator, final long denominator) {
+        return denominator == 0 ? OptionalDouble.empty() : OptionalDouble.of((double) numerator / denominator);
+    }
+    /** Averages two defined ratios. */
+    private static OptionalDouble mean(final OptionalDouble left, final OptionalDouble right) {
+        return left.isEmpty() || right.isEmpty() ? OptionalDouble.empty()
+                : OptionalDouble.of((left.getAsDouble() + right.getAsDouble()) / 2.0);
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/PairwiseMetricsTest.java

@@ -0,0 +1,43 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import org.junit.jupiter.api.DisplayName;
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+
+/** Formula and degenerate-case tests for aggregate pairwise metrics. */
+@Tag("unit")
+@DisplayName("Pairwise aggregate metrics")
+final class PairwiseMetricsTest {
+    /** Verifies all formulas use the supplied raw confusion counts. */
+    @Test @DisplayName("Aggregate metrics are calculated from raw confusion counts")
+    void formulas() {
+        final PairwiseMetrics metrics = new PairwiseMetrics(8, 2, 4, 16);
+        assertEquals(0.8, metrics.precision().orElseThrow(), 1.0e-12);
+        assertEquals(8.0 / 12.0, metrics.recall().orElseThrow(), 1.0e-12);
+        assertEquals(16.0 / 18.0, metrics.specificity().orElseThrow(), 1.0e-12);
+        assertEquals(24.0 / 30.0, metrics.accuracy().orElseThrow(), 1.0e-12);
+        assertEquals(8.0 / 14.0, metrics.jaccard().orElseThrow(), 1.0e-12);
+        assertEquals(6.0 / 30.0, metrics.errorRate().orElseThrow(), 1.0e-12);
+        assertTrue(metrics.f05().orElseThrow() > metrics.f2().orElseThrow());
+    }
+
+    /** Verifies a perfect nondegenerate relation reaches every applicable maximum. */
+    @Test @DisplayName("Perfect confusion counts produce maximum defined scores")
+    void perfect() {
+        final PairwiseMetrics metrics = new PairwiseMetrics(10, 0, 0, 20);
+        assertEquals(1.0, metrics.f05().orElseThrow()); assertEquals(1.0, metrics.f1().orElseThrow());
+        assertEquals(1.0, metrics.f2().orElseThrow()); assertEquals(1.0, metrics.matthewsCorrelationCoefficient().orElseThrow());
+        assertEquals(1.0, metrics.balancedAccuracy().orElseThrow());
+    }
+
+    /** Verifies undefined denominators remain explicit missing values. */
+    @Test @DisplayName("Degenerate zero denominators remain undefined")
+    void undefined() {
+        final PairwiseMetrics metrics = new PairwiseMetrics(0, 0, 0, 0);
+        assertTrue(metrics.precision().isEmpty()); assertTrue(metrics.recall().isEmpty());
+        assertTrue(metrics.matthewsCorrelationCoefficient().isEmpty());
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/PartitionMetrics.java

@@ -0,0 +1,8 @@
+package org.egothor.stemmer.benchmark.quality;
+
+/**
+ * Immutable strict-partition comparison metrics. Values use the arithmetic-mean
+ * normalization for normalized mutual information and are applicable only to primary output.
+ */
+record PartitionMetrics(double adjustedRandIndex, double homogeneity, double completeness,
+        double vMeasure, double normalizedMutualInformation) { }

src/test/java/org/egothor/stemmer/benchmark/quality/ProcessingMode.java

@@ -0,0 +1,32 @@
+package org.egothor.stemmer.benchmark.quality;
+
+/** Selects the gold-standard groups included in a stemming-quality scenario. */
+public enum ProcessingMode {
+    /** Includes every parsed dictionary group. */
+    ALL_WORDS,
+    /** Includes only groups containing no uppercase or titlecase Unicode code point. */
+    LOWERCASE_GROUPS_ONLY;
+
+    /**
+     * Tests whether a group is eligible for this mode.
+     *
+     * @param forms distinct word forms in the group, never {@code null}
+     * @return {@code true} when the complete group is eligible
+     */
+    public boolean includes(final Iterable<String> forms) {
+        if (this == ALL_WORDS) {
+            return true;
+        }
+        for (String form : forms) {
+            int offset = 0;
+            while (offset < form.length()) {
+                final int codePoint = form.codePointAt(offset);
+                if (Character.isUpperCase(codePoint) || Character.isTitleCase(codePoint)) {
+                    return false;
+                }
+                offset += Character.charCount(codePoint);
+            }
+        }
+        return true;
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/QualityAudit.java

@@ -0,0 +1,157 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import java.io.IOException;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.ArrayList;
+import java.util.Comparator;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Locale;
+import java.util.Map;
+
+import org.egothor.stemmer.benchmark.QualityStemmerMatrix.Candidate;
+
+/** Produces deterministic scenario and group-contribution diagnostics for audit runs. */
+final class QualityAudit {
+    /** Utility class. */
+    private QualityAudit() {
+        throw new AssertionError("No instances.");
+    }
+
+    /**
+     * Evaluates one scenario and retains its highest under-stemming contributors.
+     *
+     * @param candidate authoritative JMH candidate
+     * @param mode processing mode
+     * @param groups parsed dictionary groups
+     * @param limit maximum listed contributors
+     * @return immutable audited scenario
+     * @throws IOException if the candidate adapter fails
+     */
+    static Scenario evaluate(final Candidate candidate, final ProcessingMode mode,
+            final List<GoldStandardGroup> groups, final int limit) throws IOException {
+        final List<GoldStandardGroup> includedGroups = groups.stream().filter(group -> mode.includes(group.forms())).toList();
+        final List<String> forms = new ArrayList<>();
+        for (GoldStandardGroup group : includedGroups) {
+            forms.addAll(group.forms());
+        }
+        final String[] outputs = candidate.createStemmer().stem(forms.toArray(String[]::new));
+        if (outputs.length != forms.size()) {
+            throw new IOException("Invalid audit output count for stemmer " + candidate.name() + ", language "
+                    + candidate.language() + ", and processing mode " + mode + ".");
+        }
+        final int[] outputIndex = {0};
+        final QualityResult result = QualityEvaluator.evaluate(candidate.name(), candidate.language().name(), mode,
+                groups, word -> outputs[outputIndex[0]++]);
+        final List<Contributor> contributors = new ArrayList<>();
+        long exactMatches = 0;
+        int offset = 0;
+        final List<Integer> sizes = new ArrayList<>();
+        for (GoldStandardGroup group : includedGroups) {
+            final Map<String, List<String>> formsByStem = new LinkedHashMap<>();
+            final String expected = group.forms().get(0);
+            long mergedPairs = 0;
+            for (String form : group.forms()) {
+                final String output = outputs[offset++];
+                formsByStem.computeIfAbsent(output, ignored -> new ArrayList<>()).add(form);
+                if (expected.equals(output)) {
+                    exactMatches++;
+                }
+            }
+            for (List<String> stemForms : formsByStem.values()) {
+                mergedPairs = Math.addExact(mergedPairs, QualityEvaluator.chooseTwo(stemForms.size()));
+            }
+            final long possible = QualityEvaluator.chooseTwo(group.forms().size());
+            final long errors = Math.subtractExact(possible, mergedPairs);
+            sizes.add(group.forms().size());
+            if (errors > 0) {
+                contributors.add(new Contributor(group.rowNumber(), group.forms().size(), formsByStem, errors, possible));
+            }
+        }
+        contributors.sort(Comparator.comparingLong(Contributor::errorPairs).reversed()
+                .thenComparingInt(Contributor::rowNumber));
+        final long contributionSum = contributors.stream().mapToLong(Contributor::errorPairs).reduce(0L, Math::addExact);
+        if (contributionSum != result.underErrorPairs()) {
+            throw new IOException("The summed group contributions do not equal the optimized under-stemming total for "
+                    + candidate.name() + ", " + candidate.language() + ", and " + mode + ".");
+        }
+        sizes.sort(Integer::compareTo);
+        final double mean = sizes.stream().mapToInt(Integer::intValue).average().orElse(0.0);
+        final double median = median(sizes);
+        return new Scenario(result, candidate.language().resourcePath(), exactMatches, forms.size(),
+                sizes.isEmpty() ? 0 : sizes.get(0), sizes.isEmpty() ? 0 : sizes.get(sizes.size() - 1), mean, median,
+                List.copyOf(contributors.subList(0, Math.min(limit, contributors.size()))), contributionSum);
+    }
+
+    /** Writes all audited scenarios to a fresh UTF-8 Markdown file. */
+    static void write(final Path path, final List<Scenario> scenarios) throws IOException {
+        final StringBuilder text = new StringBuilder(8192);
+        text.append("# Stemming-quality audit\n\nThis report uses original dictionary forms and the exact JMH candidate adapters. Exact-output counts compare outputs with the first parsed field of each group; they are not interchangeable with the existing JMH exact-root counters when that corpus lowercases dictionary fields.\n\n");
+        for (Scenario scenario : scenarios.stream().sorted(Comparator.comparing(item -> item.result(), QualityResult.ORDER)).toList()) {
+            final QualityResult result = scenario.result();
+            text.append("## ").append(result.stemmer()).append(" / ").append(result.language()).append(" / ")
+                    .append(result.processingMode()).append("\n\n")
+                    .append("- Dictionary source: `").append(scenario.dictionarySource()).append("`\n")
+                    .append("- Processed dictionary rows: ").append(result.appliedDictionaryRows()).append("\n")
+                    .append("- Processed unique word forms: ").append(result.processedWordForms()).append("\n")
+                    .append("- Singleton dictionary rows: ").append(result.singletonDictionaryRows()).append("\n")
+                    .append("- Dictionary rows contributing under-stemming pairs: ").append(result.dictionaryRowsContributingUnderPairs()).append("\n")
+                    .append("- Group size minimum / maximum / mean / median: ").append(scenario.minimumGroupSize()).append(" / ")
+                    .append(scenario.maximumGroupSize()).append(" / ").append(String.format(Locale.ROOT, "%.6f", scenario.meanGroupSize()))
+                    .append(" / ").append(String.format(Locale.ROOT, "%.6f", scenario.medianGroupSize())).append("\n")
+                    .append("- Exact first-field matches: ").append(scenario.exactMatches()).append(" / ").append(scenario.exactDenominator()).append("\n")
+                    .append("- Under-stemming pairs: ").append(result.underErrorPairs()).append(" / ").append(result.underPossiblePairs()).append("\n")
+                    .append("- Over-stemming pairs: ").append(result.overErrorPairs()).append(" / ").append(result.overPossiblePairs()).append("\n")
+                    .append("- Independently summed under-stemming contributions: ").append(scenario.contributionSum()).append("\n\n")
+                    .append("### Highest under-stemming contributors\n\n");
+            for (Contributor contributor : scenario.contributors()) {
+                text.append("#### Dictionary row ").append(contributor.rowNumber()).append("\n\n")
+                        .append("Unique forms: ").append(contributor.groupSize()).append("; distinct predicted stems: ")
+                        .append(contributor.formsByStem().size()).append("; contribution: ").append(contributor.errorPairs())
+                        .append(" / ").append(contributor.possiblePairs()).append(" pairs.\n\n");
+                for (Map.Entry<String, List<String>> entry : contributor.formsByStem().entrySet()) {
+                    text.append("- Predicted stem `").append(escape(entry.getKey())).append("` (").append(entry.getValue().size())
+                            .append("): ").append(entry.getValue().stream().map(QualityAudit::quoted).toList()).append("\n");
+                }
+                text.append('\n');
+            }
+        }
+        final Path parent = path.toAbsolutePath().getParent();
+        if (parent != null) {
+            Files.createDirectories(parent);
+        }
+        Files.writeString(path, text.toString(), StandardCharsets.UTF_8);
+    }
+
+    /** Calculates the conventional median of a sorted integer list. */
+    private static double median(final List<Integer> sorted) {
+        if (sorted.isEmpty()) {
+            return 0.0;
+        }
+        final int middle = sorted.size() / 2;
+        return sorted.size() % 2 == 0 ? (sorted.get(middle - 1) + sorted.get(middle)) / 2.0 : sorted.get(middle);
+    }
+
+    /** Escapes Markdown code-span delimiters. */
+    private static String escape(final String value) {
+        return value.replace("`", "\\`");
+    }
+
+    /** Quotes one original dictionary form for Markdown diagnostics. */
+    private static String quoted(final String value) {
+        return "`" + escape(value) + "`";
+    }
+
+    /** Immutable complete audit summary for one scenario. */
+    record Scenario(QualityResult result, String dictionarySource, long exactMatches, long exactDenominator,
+            int minimumGroupSize, int maximumGroupSize, double meanGroupSize, double medianGroupSize,
+            List<Contributor> contributors, long contributionSum) {
+    }
+
+    /** Immutable contribution of one gold-standard group. */
+    record Contributor(int rowNumber, int groupSize, Map<String, List<String>> formsByStem,
+            long errorPairs, long possiblePairs) {
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/QualityEvaluator.java

@@ -0,0 +1,197 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import java.io.IOException;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Map;
+import java.util.Objects;
+import java.util.Set;
+import java.util.ArrayList;
+
+import org.egothor.stemmer.benchmark.QualityStemmerMatrix.BatchStemmer;
+
+/** Evaluates pairwise partition agreement using aggregated frequencies, never explicit pairs. */
+public final class QualityEvaluator {
+    /** Utility class. */
+    private QualityEvaluator() { throw new AssertionError("No instances."); }
+
+    /**
+     * Evaluates one scenario in time proportional to forms and group-to-stem associations.
+     * All combinatorial arithmetic is checked and overflow is reported.
+     *
+     * @param stemmerName stable stemmer name
+     * @param language stable language identifier
+     * @param mode processing mode
+     * @param groups parsed gold-standard groups
+     * @param stemmer stemmer implementation
+     * @return immutable metric result
+     */
+    public static QualityResult evaluate(final String stemmerName, final String language, final ProcessingMode mode,
+            final Iterable<GoldStandardGroup> groups, final StemmerFunction stemmer) {
+        Objects.requireNonNull(groups, "groups");
+        Objects.requireNonNull(stemmer, "stemmer");
+        final Map<String, Long> global = new HashMap<>();
+        long rows = 0;
+        long words = 0;
+        long singletonRows = 0;
+        long pairRows = 0;
+        long underPossible = 0;
+        long withinSameStem = 0;
+        final Set<String> stems = new HashSet<>();
+        final Map<String, Long> local = new HashMap<>();
+        final List<Map<String, Long>> contingency = new ArrayList<>();
+        final List<Long> groupSizes = new ArrayList<>();
+        for (GoldStandardGroup group : groups) {
+            final List<String> forms = group.forms();
+            if (!mode.includes(forms)) {
+                continue;
+            }
+            rows = add(rows, 1, "applied dictionary rows");
+            words = add(words, forms.size(), "processed word forms");
+            if (forms.size() == 1) {
+                singletonRows = add(singletonRows, 1, "singleton dictionary rows");
+            } else {
+                pairRows = add(pairRows, 1, "dictionary rows contributing under-stemming pairs");
+            }
+            underPossible = add(underPossible, chooseTwo(forms.size()), "under-stemming possible pairs");
+            local.clear();
+            for (String form : forms) {
+                final String output;
+                try {
+                    output = stemmer.stem(form);
+                } catch (IOException exception) {
+                    throw failure(stemmerName, language, mode, group.rowNumber(), form,
+                            "the stemmer threw an exception", exception);
+                }
+                if (output == null) {
+                    throw failure(stemmerName, language, mode, group.rowNumber(), form,
+                            "the stemmer returned null", null);
+                }
+                local.merge(output, 1L, (left, right) -> add(left, right, "group-to-stem frequency"));
+                global.merge(output, 1L, (left, right) -> add(left, right, "global stem frequency"));
+                stems.add(output);
+            }
+            for (long frequency : local.values()) {
+                withinSameStem = add(withinSameStem, chooseTwo(frequency), "within-group merged pairs");
+            }
+            contingency.add(Map.copyOf(local));
+            groupSizes.add((long) forms.size());
+        }
+        long allPairs = chooseTwo(words);
+        long overPossible = subtract(allPairs, underPossible, "over-stemming possible pairs");
+        long allSameStem = 0;
+        for (long frequency : global.values()) {
+            allSameStem = add(allSameStem, chooseTwo(frequency), "same-stem pairs");
+        }
+        final long underError = subtract(underPossible, withinSameStem, "under-stemming error pairs");
+        final long overError = subtract(allSameStem, withinSameStem, "over-stemming error pairs");
+        final PartitionMetrics partition = partitionMetrics(words, underPossible, allSameStem,
+                withinSameStem, groupSizes, global, contingency);
+        return new QualityResult(stemmerName, language, mode, OutputPolicy.PRIMARY_OUTPUT,
+                rows, words, singletonRows, pairRows, words, 0, words == 0 ? 0 : 1, words, stems.size(), overError, overPossible,
+                underError, underPossible, partition);
+    }
+
+    /** Calculates strict-partition metrics from the exact contingency table. */
+    private static PartitionMetrics partitionMetrics(final long words, final long rowPairs, final long columnPairs,
+            final long indexPairs, final List<Long> groupSizes, final Map<String, Long> global,
+            final List<Map<String, Long>> contingency) {
+        if (words == 0) { return new PartitionMetrics(0.0, 0.0, 0.0, 0.0, 0.0); }
+        final double totalPairs = chooseTwo(words);
+        final double expected = totalPairs == 0.0 ? 0.0 : (double) rowPairs * columnPairs / totalPairs;
+        final double maximum = (rowPairs + (double) columnPairs) / 2.0;
+        final double adjustedRand = maximum == expected ? 1.0 : (indexPairs - expected) / (maximum - expected);
+        final double goldEntropy = entropy(words, groupSizes);
+        final double predictedEntropy = entropy(words, global.values());
+        double mutualInformation = 0.0;
+        for (int group = 0; group < contingency.size(); group++) {
+            final long groupSize = groupSizes.get(group);
+            for (Map.Entry<String, Long> cell : contingency.get(group).entrySet()) {
+                final double frequency = cell.getValue();
+                mutualInformation += frequency / words * Math.log(frequency * words
+                        / (groupSize * (double) global.get(cell.getKey())));
+            }
+        }
+        final double homogeneity = goldEntropy == 0.0 ? 1.0 : mutualInformation / goldEntropy;
+        final double completeness = predictedEntropy == 0.0 ? 1.0 : mutualInformation / predictedEntropy;
+        final double vMeasure = homogeneity + completeness == 0.0 ? 0.0
+                : 2.0 * homogeneity * completeness / (homogeneity + completeness);
+        final double nmiDenominator = (goldEntropy + predictedEntropy) / 2.0;
+        final double nmi = nmiDenominator == 0.0 ? 1.0 : mutualInformation / nmiDenominator;
+        return new PartitionMetrics(adjustedRand, homogeneity, completeness, vMeasure, nmi);
+    }
+
+    /** Calculates natural-log entropy from category frequencies. */
+    private static double entropy(final long total, final Iterable<Long> frequencies) {
+        double weightedLogs = 0.0;
+        for (long frequency : frequencies) { weightedLogs += frequency * Math.log(frequency); }
+        return Math.log(total) - weightedLogs / total;
+    }
+
+    /**
+     * Evaluates one scenario through an authoritative JMH batch adapter.
+     * The temporary input and output arrays are required to preserve TokenStream
+     * lifecycle and preprocessing semantics used by the JMH comparison.
+     *
+     * @param stemmerName stable JMH candidate name
+     * @param language registered dictionary language
+     * @param mode processing mode
+     * @param groups parsed gold-standard groups
+     * @param stemmer scenario-confined batch adapter
+     * @return immutable pairwise result
+     * @throws IOException when the benchmark adapter fails
+     */
+    public static QualityResult evaluateBatch(final String stemmerName, final String language,
+            final ProcessingMode mode, final List<GoldStandardGroup> groups, final BatchStemmer stemmer)
+            throws IOException {
+        final List<String> included = new ArrayList<>();
+        for (GoldStandardGroup group : groups) {
+            if (mode.includes(group.forms())) {
+                included.addAll(group.forms());
+            }
+        }
+        final String[] outputs = stemmer.stem(included.toArray(String[]::new));
+        if (outputs == null || outputs.length != included.size()) {
+            throw new IOException("JMH stemmer " + stemmerName + " returned an invalid output batch for language "
+                    + language + " and processing mode " + mode + ".");
+        }
+        final int[] index = {0};
+        return evaluate(stemmerName, language, mode, groups, word -> {
+            final String output = outputs[index[0]++];
+            if (output == null) {
+                throw new IOException("JMH stemmer " + stemmerName + " returned null for language " + language
+                        + ", processing mode " + mode + ", and word form '" + word + "'.");
+            }
+            return output;
+        });
+    }
+
+    /** Calculates C2(n) with checked arithmetic. */
+    /* default */ static long chooseTwo(final long value) {
+        if (value < 0) { throw new IllegalArgumentException("Pair population must not be negative."); }
+        try {
+            return value % 2 == 0 ? Math.multiplyExact(value / 2, value - 1)
+                    : Math.multiplyExact(value, (value - 1) / 2);
+        } catch (ArithmeticException exception) {
+            throw new IllegalStateException("Arithmetic overflow while calculating unordered word-form pairs.", exception);
+        }
+    }
+    /** Checked addition with metric context. */
+    private static long add(final long left, final long right, final String context) {
+        try { return Math.addExact(left, right); }
+        catch (ArithmeticException exception) { throw new IllegalStateException("Arithmetic overflow in " + context + ".", exception); }
+    }
+    /** Checked subtraction with metric context. */
+    private static long subtract(final long left, final long right, final String context) {
+        try { return Math.subtractExact(left, right); }
+        catch (ArithmeticException exception) { throw new IllegalStateException("Arithmetic overflow in " + context + ".", exception); }
+    }
+    /** Builds a contextual failure without producing a partial result. */
+    private static IllegalStateException failure(final String stemmer, final String language,
+            final ProcessingMode mode, final int row, final String form, final String reason, final Exception cause) {
+        final String message = "Quality evaluation failed for stemmer " + stemmer + ", language " + language
+                + ", processing mode " + mode + ", dictionary row " + row + ", word form '" + form + "': " + reason + ".";
+        return cause == null ? new IllegalStateException(message) : new IllegalStateException(message, cause);
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/QualityEvaluatorTest.java

@@ -0,0 +1,178 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.util.List;
+import java.util.Map;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.Random;
+
+import org.junit.jupiter.api.DisplayName;
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+
+/** Mathematical and filtering tests for pairwise quality evaluation. */
+@Tag("unit")
+@DisplayName("Pairwise stemming-quality evaluator")
+final class QualityEvaluatorTest {
+    /** Verifies a perfect partition. */
+    @Test @DisplayName("A perfect predicted partition has no errors")
+    void perfectPartition() {
+        final QualityResult result = evaluate(List.of(group(1, "a", "b"), group(2, "c", "d")),
+                Map.of("a", "x", "b", "x", "c", "y", "d", "y"));
+        assertEquals(0, result.overErrorPairs()); assertEquals(4, result.overPossiblePairs());
+        assertEquals(0, result.underErrorPairs()); assertEquals(2, result.underPossiblePairs());
+        assertEquals(2, result.distinctOutputStems());
+        assertEquals(1.0, result.partitionMetrics().adjustedRandIndex(), 1.0e-12);
+        assertEquals(1.0, result.partitionMetrics().homogeneity(), 1.0e-12);
+        assertEquals(1.0, result.partitionMetrics().completeness(), 1.0e-12);
+        assertEquals(1.0, result.partitionMetrics().vMeasure(), 1.0e-12);
+        assertEquals(1.0, result.partitionMetrics().normalizedMutualInformation(), 1.0e-12);
+    }
+    /** Verifies partial merge and pure under-stemming pair counts. */
+    @Test @DisplayName("A partial within-group merge is counted by pairs")
+    void partialMerge() {
+        final QualityResult result = evaluate(List.of(group(1, "a", "b", "c"), group(2, "d")),
+                Map.of("a", "x", "b", "x", "c", "z", "d", "q"));
+        assertEquals(2, result.underErrorPairs()); assertEquals(3, result.underPossiblePairs());
+        assertEquals(0, result.overErrorPairs()); assertEquals(3, result.overPossiblePairs());
+    }
+    /** Verifies multi-group over-stemming combinatorics. */
+    @Test @DisplayName("Several gold groups colliding in one stem count every cross-group pair")
+    void multipleGroupsCollide() {
+        final QualityResult result = evaluate(List.of(group(1, "a", "b"), group(2, "c"), group(3, "d", "e", "f")),
+                Map.of("a", "x", "b", "x", "c", "x", "d", "x", "e", "x", "f", "x"));
+        assertEquals(11, result.overErrorPairs()); assertEquals(11, result.overPossiblePairs());
+        assertEquals(0, result.underErrorPairs());
+    }
+    /** Verifies combined split and collision counts. */
+    @Test @DisplayName("Combined over-stemming and under-stemming are independent")
+    void combinedErrors() {
+        final QualityResult result = evaluate(List.of(group(1, "a", "b", "c"), group(2, "d", "e")),
+                Map.of("a", "x", "b", "x", "c", "y", "d", "y", "e", "y"));
+        assertEquals(2, result.overErrorPairs()); assertEquals(6, result.overPossiblePairs());
+        assertEquals(2, result.underErrorPairs()); assertEquals(4, result.underPossiblePairs());
+    }
+    /** Verifies duplicate scope and singleton undefined denominator. */
+    @Test @DisplayName("Duplicates are removed only within a group and singleton under-stemming is undefined")
+    void duplicateScope() {
+        final QualityResult result = evaluate(List.of(group(1, "same", "same"), group(2, "same")), Map.of("same", "x"));
+        assertEquals(2, result.processedWordForms()); assertEquals(1, result.overErrorPairs());
+        assertTrue(result.underPercentage().isEmpty());
+    }
+    /** Verifies the zero over-stemming denominator. */
+    @Test @DisplayName("One gold group has an undefined over-stemming percentage")
+    void zeroOverDenominator() {
+        final QualityResult result = evaluate(List.of(group(1, "a", "b")), Map.of("a", "x", "b", "y"));
+        assertTrue(result.overPercentage().isEmpty()); assertFalse(result.underPercentage().isEmpty());
+    }
+    /** Verifies Unicode code-point filtering and uncased data. */
+    @Test @DisplayName("Lowercase filtering detects uppercase and titlecase code points without excluding uncased symbols")
+    void lowercaseFiltering() {
+        assertTrue(ProcessingMode.LOWERCASE_GROUPS_ONLY.includes(List.of("žluťoučký-123", "தமிழ்")));
+        assertFalse(ProcessingMode.LOWERCASE_GROUPS_ONLY.includes(List.of("Upper")));
+        assertFalse(ProcessingMode.LOWERCASE_GROUPS_ONLY.includes(List.of("Džungla")));
+        assertFalse(ProcessingMode.LOWERCASE_GROUPS_ONLY.includes(List.of("a\uD801\uDC00")));
+    }
+    /** Verifies contextual stemmer failures. */
+    @Test @DisplayName("Stemmer exceptions contain complete scenario context")
+    void stemmerFailure() {
+        final IllegalStateException exception = assertThrows(IllegalStateException.class,
+                () -> QualityEvaluator.evaluate("Broken", "TEST", ProcessingMode.ALL_WORDS,
+                        List.of(group(7, "word")), word -> { throw new IOException("failure"); }));
+        assertTrue(exception.getMessage().contains("dictionary row 7")); assertTrue(exception.getMessage().contains("word form 'word'"));
+    }
+    /** Verifies the largest safe and first overflowing combinatorial values. */
+    @Test @DisplayName("Pair calculation detects arithmetic overflow")
+    void arithmeticBoundary() {
+        assertEquals(4_611_686_013_944_624_251L, QualityEvaluator.chooseTwo(3_037_000_499L));
+        assertThrows(IllegalStateException.class, () -> QualityEvaluator.chooseTwo(Long.MAX_VALUE));
+    }
+
+    /** Demonstrates the documented denominator difference from exact accuracy. */
+    @Test @DisplayName("Ninety-nine percent exact accuracy can coexist with sixteen percent pairwise under-stemming")
+    void exactAccuracyAndPairwiseRateUseDifferentDenominators() {
+        final List<GoldStandardGroup> groups = new ArrayList<>();
+        final Map<String, String> stems = new HashMap<>();
+        for (int index = 0; index < 88; index++) {
+            final String form = "singleton-" + index;
+            groups.add(group(index + 1, form));
+            stems.put(form, form);
+        }
+        final String[] largeGroup = new String[12];
+        for (int index = 0; index < largeGroup.length; index++) {
+            largeGroup[index] = "form-" + index;
+            stems.put(largeGroup[index], index == 11 ? "different" : "shared");
+        }
+        groups.add(group(89, largeGroup));
+        final QualityResult result = evaluate(groups, stems);
+        assertEquals(100, result.processedWordForms());
+        assertEquals(66, result.underPossiblePairs());
+        assertEquals(11, result.underErrorPairs());
+        assertEquals(16.666666666666668, result.underPercentage().orElseThrow(), 0.000000000000001);
+    }
+
+    /** Compares the optimized accumulator with an independent explicit pair oracle. */
+    @Test @DisplayName("Deterministic randomized partitions agree with a brute-force pair oracle")
+    void randomizedOracleAgreement() {
+        final Random random = new Random(0x52414449584f52L);
+        for (int trial = 0; trial < 250; trial++) {
+            final List<GoldStandardGroup> groups = new ArrayList<>();
+            final Map<String, String> stems = new HashMap<>();
+            final Map<String, Integer> gold = new HashMap<>();
+            final int groupCount = 1 + random.nextInt(7);
+            int formIndex = 0;
+            for (int groupIndex = 0; groupIndex < groupCount; groupIndex++) {
+                final int size = 1 + random.nextInt(6);
+                final String[] forms = new String[size];
+                for (int index = 0; index < size; index++) {
+                    final String form = "t" + trial + "-f" + formIndex++;
+                    forms[index] = form;
+                    gold.put(form, groupIndex);
+                    stems.put(form, "s" + random.nextInt(6));
+                }
+                groups.add(group(groupIndex + 1, forms));
+            }
+            final QualityResult optimized = evaluate(groups, stems);
+            final long[] oracle = bruteForce(new ArrayList<>(gold.keySet()), gold, stems);
+            assertEquals(oracle[0], optimized.underErrorPairs(), "Under-stemming errors differed in trial " + trial);
+            assertEquals(oracle[1], optimized.underPossiblePairs(), "Under-stemming denominator differed in trial " + trial);
+            assertEquals(oracle[2], optimized.overErrorPairs(), "Over-stemming errors differed in trial " + trial);
+            assertEquals(oracle[3], optimized.overPossiblePairs(), "Over-stemming denominator differed in trial " + trial);
+        }
+    }
+
+    /** Explicit quadratic oracle used only for small controlled test data. */
+    private static long[] bruteForce(final List<String> forms, final Map<String, Integer> gold,
+            final Map<String, String> stems) {
+        long underError = 0;
+        long underPossible = 0;
+        long overError = 0;
+        long overPossible = 0;
+        for (int left = 0; left < forms.size(); left++) {
+            for (int right = left + 1; right < forms.size(); right++) {
+                final boolean sameGold = gold.get(forms.get(left)).equals(gold.get(forms.get(right)));
+                final boolean sameStem = stems.get(forms.get(left)).equals(stems.get(forms.get(right)));
+                if (sameGold) {
+                    underPossible++;
+                    if (!sameStem) { underError++; }
+                } else {
+                    overPossible++;
+                    if (sameStem) { overError++; }
+                }
+            }
+        }
+        return new long[] {underError, underPossible, overError, overPossible};
+    }
+    /** Builds a group. */
+    private static GoldStandardGroup group(final int row, final String... forms) { return new GoldStandardGroup(row, List.of(forms)); }
+    /** Runs the common synthetic evaluator. */
+    private static QualityResult evaluate(final List<GoldStandardGroup> groups, final Map<String, String> stems) {
+        return QualityEvaluator.evaluate("Synthetic", "TEST", ProcessingMode.ALL_WORDS, groups, stems::get);
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/QualityReportWriter.java

@@ -0,0 +1,256 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import java.io.IOException;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.ArrayList;
+import java.util.Comparator;
+import java.util.List;
+import java.util.Locale;
+import java.util.OptionalDouble;
+import java.util.Set;
+import java.util.TreeSet;
+
+import org.egothor.stemmer.benchmark.QualityStemmerMatrix.Candidate;
+
+/** Writes deterministic UTF-8 Markdown and CSV quality reports. */
+public final class QualityReportWriter {
+    private static final String TABLE_DELIMITER = " | ";
+    /** Utility class. */
+    private QualityReportWriter() { throw new AssertionError("No instances."); }
+
+    /** Writes a report without external coverage metadata for focused formatting tests. */
+    public static void writeMarkdown(final Path path, final Iterable<QualityResult> input,
+            final boolean filtered) throws IOException {
+        writeMarkdown(path, input, filtered, new LanguageUniverse(java.util.Map.of(), List.of(), List.of()),
+                List.of(), sorted(input).size(), "PAIRWISE_F05");
+    }
+
+    /** Writes the human-readable report with methodology and required table columns. */
+    public static void writeMarkdown(final Path path, final Iterable<QualityResult> input, final boolean filtered,
+            final LanguageUniverse universe, final List<Candidate> candidates, final int expectedRows,
+            final String rankMetric) throws IOException {
+        final List<QualityResult> rows = sorted(input);
+        final StringBuilder text = new StringBuilder(4096);
+        text.append("# Stemming quality\n\n");
+        if (filtered) {
+            text.append("> This is a filtered analytical report and is not the complete JMH candidate matrix.\n\n");
+        }
+        text.append("## Methodology\n\nEach parsed multilingual dictionary row is a gold-standard equivalence class. Exact duplicates are removed only within that row. `PRIMARY_OUTPUT` is the deterministic JMH partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: within-row sets must intersect, while a cross-row error occurs only for two equal singleton sets. `ALL_CANDIDATES` activates the complete overlap relation: within-row disjoint sets are false negatives and cross-row intersections are false positives. A shared pair is counted once. Candidate policies need not define partitions.\n\nTP is a related within-row pair, FN is an unrelated within-row pair, FP is a related cross-row pair, and TN is an unrelated cross-row pair. Under-stemming is FN/(TP+FN); over-stemming is FP/(TN+FP), so their denominators differ. F0.5 emphasizes precision, F1 balances precision and recall, and F2 emphasizes recall. Undefined values are `n/a`. Percentages and scores use `Locale.ROOT`.\n\n| Stemmer | Language | Dictionary mode | Output policy | Applied dictionary rows | Processed word forms | Distinct output stems | Over-stemming | Under-stemming | Pairwise F0.5 | Pairwise F1 | Pairwise F2 |\n|---|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|\n");
+        for (QualityResult row : rows) {
+            text.append("| ").append(escapeMarkdown(row.stemmer())).append(TABLE_DELIMITER)
+                    .append(escapeMarkdown(row.language())).append(TABLE_DELIMITER).append(row.processingMode()).append(TABLE_DELIMITER)
+                    .append(row.outputPolicy()).append(TABLE_DELIMITER).append(row.appliedDictionaryRows()).append(TABLE_DELIMITER)
+                    .append(row.processedWordForms()).append(TABLE_DELIMITER).append(row.distinctOutputStems()).append(TABLE_DELIMITER)
+                    .append(humanMetric(row.overErrorPairs(), row.overPossiblePairs(), row.overPercentage())).append(TABLE_DELIMITER)
+                    .append(humanMetric(row.underErrorPairs(), row.underPossiblePairs(), row.underPercentage())).append(TABLE_DELIMITER)
+                    .append(score(row.pairwiseMetrics().f05())).append(TABLE_DELIMITER)
+                    .append(score(row.pairwiseMetrics().f1())).append(TABLE_DELIMITER)
+                    .append(score(row.pairwiseMetrics().f2())).append(" |\n");
+        }
+        appendComparisons(text, rows);
+        appendCoverage(text, universe, candidates, expectedRows, rows.size());
+        appendRankings(text, rows, rankMetric);
+        appendSummaries(text, rows);
+        text.append("\n## Reproducibility environment\n\n- JDK: `").append(System.getProperty("java.version"))
+                .append("`\n- Operating system: `").append(System.getProperty("os.name")).append(' ')
+                .append(System.getProperty("os.version")).append("`\n");
+        write(path, text.toString());
+    }
+
+    /** Writes machine-readable counts and separate percentage fields. */
+    public static void writeCsv(final Path path, final Iterable<QualityResult> input) throws IOException {
+        final StringBuilder text = new StringBuilder(4096);
+        text.append("Stemmer,Language,Dictionary mode,Output policy,Applied dictionary rows,Processed word forms,Singleton dictionary rows,Forms with one candidate,Forms with multiple candidates,Maximum candidates for one form,Total candidate assignments,Distinct output stems,True-positive pairs,False-positive pairs,False-negative pairs,True-negative pairs,Over-stemming error pairs,Over-stemming possible pairs,Over-stemming percentage,Under-stemming error pairs,Under-stemming possible pairs,Under-stemming percentage,Pairwise precision,Pairwise recall,Pairwise specificity,Pairwise accuracy,Balanced accuracy,Pairwise F0.5,Pairwise F1,Pairwise F2,Jaccard index,Fowlkes-Mallows index,Matthews correlation coefficient,Pairwise error rate,Adjusted Rand Index,Homogeneity,Completeness,V-measure,Normalized mutual information\n");
+        for (QualityResult row : sorted(input)) {
+            final PairwiseMetrics metrics = row.pairwiseMetrics();
+            appendCsv(text, row.stemmer()); appendCsv(text, row.language()); appendCsv(text, row.processingMode().name());
+            appendCsv(text, row.outputPolicy().name());
+            appendCsv(text, Long.toString(row.appliedDictionaryRows())); appendCsv(text, Long.toString(row.processedWordForms()));
+            appendCsv(text, Long.toString(row.singletonDictionaryRows()));
+            appendCsv(text, Long.toString(row.formsWithOneCandidate()));
+            appendCsv(text, Long.toString(row.formsWithMultipleCandidates()));
+            appendCsv(text, Long.toString(row.maximumCandidatesForOneWord()));
+            appendCsv(text, Long.toString(row.totalCandidateAssignments()));
+            appendCsv(text, Long.toString(row.distinctOutputStems()));
+            appendCsv(text, Long.toString(metrics.truePositivePairs())); appendCsv(text, Long.toString(metrics.falsePositivePairs()));
+            appendCsv(text, Long.toString(metrics.falseNegativePairs())); appendCsv(text, Long.toString(metrics.trueNegativePairs()));
+            appendCsv(text, Long.toString(row.overErrorPairs()));
+            appendCsv(text, Long.toString(row.overPossiblePairs())); appendCsv(text, machinePercent(row.overPercentage()));
+            appendCsv(text, Long.toString(row.underErrorPairs())); appendCsv(text, Long.toString(row.underPossiblePairs()));
+            appendCsv(text, machinePercent(row.underPercentage()));
+            appendCsv(text, machineScore(metrics.precision())); appendCsv(text, machineScore(metrics.recall()));
+            appendCsv(text, machineScore(metrics.specificity())); appendCsv(text, machineScore(metrics.accuracy()));
+            appendCsv(text, machineScore(metrics.balancedAccuracy())); appendCsv(text, machineScore(metrics.f05()));
+            appendCsv(text, machineScore(metrics.f1())); appendCsv(text, machineScore(metrics.f2()));
+            appendCsv(text, machineScore(metrics.jaccard())); appendCsv(text, machineScore(metrics.fowlkesMallows()));
+            appendCsv(text, machineScore(metrics.matthewsCorrelationCoefficient())); appendCsv(text, machineScore(metrics.errorRate()));
+            final PartitionMetrics partition = row.partitionMetrics();
+            appendCsv(text, partition == null ? "" : format(partition.adjustedRandIndex()));
+            appendCsv(text, partition == null ? "" : format(partition.homogeneity()));
+            appendCsv(text, partition == null ? "" : format(partition.completeness()));
+            appendCsv(text, partition == null ? "" : format(partition.vMeasure()));
+            appendCsv(text, partition == null ? "" : format(partition.normalizedMutualInformation()));
+            text.setLength(text.length() - 1); text.append('\n');
+        }
+        write(path, text.toString());
+    }
+
+    /** Appends deterministic primary-versus-candidate trade-off rows for multi-output scenarios. */
+    private static void appendComparisons(final StringBuilder text, final List<QualityResult> rows) {
+        text.append("\n## Primary-versus-candidate comparison\n\n")
+                .append("| Stemmer | Language | Dictionary mode | Primary under | Any under | All under | Repaired under | Primary over | Any over | Best-case avoided over | All over | Additional all-candidate over | Multi-candidate forms | Multi-candidate percent | Maximum candidates | Candidate assignments |\n")
+                .append("|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|\n");
+        for (QualityResult candidate : rows) {
+            if (candidate.outputPolicy() != OutputPolicy.ANY_CANDIDATE) { continue; }
+            final QualityResult primary = rows.stream().filter(row -> row.stemmer().equals(candidate.stemmer())
+                    && row.language().equals(candidate.language()) && row.processingMode() == candidate.processingMode()
+                    && row.outputPolicy() == OutputPolicy.PRIMARY_OUTPUT).findFirst().orElse(null);
+            final QualityResult all = rows.stream().filter(row -> row.stemmer().equals(candidate.stemmer())
+                    && row.language().equals(candidate.language()) && row.processingMode() == candidate.processingMode()
+                    && row.outputPolicy() == OutputPolicy.ALL_CANDIDATES).findFirst().orElse(null);
+            if (primary == null || all == null) { continue; }
+            text.append("| ").append(escapeMarkdown(candidate.stemmer())).append(TABLE_DELIMITER)
+                    .append(escapeMarkdown(candidate.language())).append(TABLE_DELIMITER).append(candidate.processingMode()).append(TABLE_DELIMITER)
+                    .append(primary.underErrorPairs()).append(TABLE_DELIMITER).append(candidate.underErrorPairs()).append(TABLE_DELIMITER)
+                    .append(all.underErrorPairs()).append(TABLE_DELIMITER)
+                    .append(primary.underErrorPairs() - candidate.underErrorPairs()).append(TABLE_DELIMITER)
+                    .append(primary.overErrorPairs()).append(TABLE_DELIMITER).append(candidate.overErrorPairs()).append(TABLE_DELIMITER)
+                    .append(primary.overErrorPairs() - candidate.overErrorPairs()).append(TABLE_DELIMITER)
+                    .append(all.overErrorPairs()).append(TABLE_DELIMITER)
+                    .append(all.overErrorPairs() - primary.overErrorPairs()).append(TABLE_DELIMITER)
+                    .append(candidate.formsWithMultipleCandidates()).append(TABLE_DELIMITER)
+                    .append(String.format(Locale.ROOT, "%.6f%%", 100.0 * candidate.formsWithMultipleCandidates()
+                            / candidate.processedWordForms())).append(TABLE_DELIMITER)
+                    .append(candidate.maximumCandidatesForOneWord()).append(TABLE_DELIMITER)
+                    .append(candidate.totalCandidateAssignments()).append(" |\n");
+        }
+    }
+
+    /** Appends validated language, adapter, policy, and row-count coverage. */
+    private static void appendCoverage(final StringBuilder text, final LanguageUniverse universe,
+            final List<Candidate> candidates, final int expectedRows, final int actualRows) {
+        text.append("\n## Matrix coverage\n\n- Discovered dictionary languages: ").append(universe.resourceDirectories()).append("\n")
+                .append("- Discovered `StemmerPatchTrieLoader.Language` values: ").append(universe.enumerationValues()).append("\n")
+                .append("- Reconciled mappings: ").append(universe.dictionaries().entrySet().stream()
+                        .sorted(java.util.Map.Entry.comparingByKey()).map(entry -> entry.getKey() + " -> " + entry.getValue().getFileName()).toList()).append("\n")
+                .append("- Discovered adapter-language mappings: ").append(candidates.size()).append("\n")
+                .append("- Expected result rows: ").append(expectedRows).append("\n")
+                .append("- Actual result rows: ").append(actualRows).append("\n\n")
+                .append("Unsupported third-party combinations are excluded because their authoritative JMH adapter metadata declares no mapping for that language. They are not emitted as zero-valued rows. Radixor is independently registered for every reconciled dictionary language.\n");
+        final java.util.Map<String, Set<String>> support = new java.util.TreeMap<>();
+        for (Candidate candidate : candidates) { support.computeIfAbsent(candidate.name(), ignored -> new TreeSet<>()).add(candidate.language().name()); }
+        text.append("\n| Adapter | Supported language count | Supported languages |\n|---|---:|---|\n");
+        support.forEach((name, languages) -> text.append("| ").append(escapeMarkdown(name)).append(TABLE_DELIMITER)
+                .append(languages.size()).append(TABLE_DELIMITER).append(languages).append(" |\n"));
+    }
+
+    /** Appends policy-separated rankings for the requested navigation metric and all required alternatives. */
+    private static void appendRankings(final StringBuilder text, final List<QualityResult> rows, final String selectedMetric) {
+        text.append("\n## Rankings\n\nThe default or selected ranking metric (`").append(selectedMetric)
+                .append("`) is a navigation choice, not a declaration of universal scientific superiority. Policies are ranked separately. Full-coverage and common-language comparisons must not be conflated.\n");
+        final List<String> metricNames = List.of("Pairwise F0.5", "Pairwise F1", "Pairwise F2", "Jaccard index",
+                "Fowlkes-Mallows index", "Matthews correlation coefficient", "Balanced accuracy", "Adjusted Rand Index");
+        for (String metric : metricNames) {
+            text.append("\n### ").append(metric).append("\n\n| Output policy | Stemmer | Language | Dictionary mode | Score |\n|---|---|---|---|---:|\n");
+            rows.stream().filter(row -> !metric.equals("Adjusted Rand Index") || row.outputPolicy() == OutputPolicy.PRIMARY_OUTPUT)
+                    .sorted(Comparator.comparingDouble((QualityResult row) -> rankingValue(row, metric)).reversed()
+                            .thenComparingDouble(row -> row.overPercentage().orElse(Double.POSITIVE_INFINITY))
+                            .thenComparingLong(QualityResult::overErrorPairs)
+                            .thenComparingDouble(row -> row.underPercentage().orElse(Double.POSITIVE_INFINITY))
+                            .thenComparing(QualityResult::stemmer).thenComparing(QualityResult::language))
+                    .limit(25).forEach(row -> text.append("| ").append(row.outputPolicy()).append(TABLE_DELIMITER)
+                            .append(escapeMarkdown(row.stemmer())).append(TABLE_DELIMITER).append(row.language()).append(TABLE_DELIMITER)
+                            .append(row.processingMode()).append(TABLE_DELIMITER).append(score(metricValue(row, metric))).append(" |\n"));
+        }
+    }
+
+    /** Returns one optional ranking metric. */
+    private static OptionalDouble metricValue(final QualityResult row, final String metric) {
+        return switch (metric) {
+            case "Pairwise F0.5" -> row.pairwiseMetrics().f05(); case "Pairwise F1" -> row.pairwiseMetrics().f1();
+            case "Pairwise F2" -> row.pairwiseMetrics().f2(); case "Jaccard index" -> row.pairwiseMetrics().jaccard();
+            case "Fowlkes-Mallows index" -> row.pairwiseMetrics().fowlkesMallows();
+            case "Matthews correlation coefficient" -> row.pairwiseMetrics().matthewsCorrelationCoefficient();
+            case "Balanced accuracy" -> row.pairwiseMetrics().balancedAccuracy();
+            case "Adjusted Rand Index" -> row.partitionMetrics() == null ? OptionalDouble.empty()
+                    : OptionalDouble.of(row.partitionMetrics().adjustedRandIndex());
+            default -> OptionalDouble.empty();
+        };
+    }
+
+    /** Appends full-coverage micro and macro summaries with explicit coverage. */
+    private static void appendSummaries(final StringBuilder text, final List<QualityResult> rows) {
+        text.append("\n## Aggregate summaries\n\nMicro values sum raw confusion counts before calculation. Macro values average defined per-language F1 values.\n\n")
+                .append("| Stemmer | Dictionary mode | Output policy | Languages | Micro F0.5 | Micro F1 | Micro F2 | Macro F1 | Macro contributing languages |\n")
+                .append("|---|---|---|---:|---:|---:|---:|---:|---:|\n");
+        final java.util.Map<String, List<QualityResult>> groups = new java.util.TreeMap<>();
+        for (QualityResult row : rows) {
+            groups.computeIfAbsent(row.stemmer() + "\u0000" + row.processingMode() + "\u0000" + row.outputPolicy(),
+                    ignored -> new ArrayList<>()).add(row);
+        }
+        for (List<QualityResult> group : groups.values()) {
+            final QualityResult first = group.get(0); long tp = 0; long fp = 0; long fn = 0; long tn = 0;
+            double macroF1 = 0.0; int macroCount = 0; final Set<String> languages = new TreeSet<>();
+            for (QualityResult row : group) {
+                final PairwiseMetrics metrics = row.pairwiseMetrics();
+                tp = Math.addExact(tp, metrics.truePositivePairs()); fp = Math.addExact(fp, metrics.falsePositivePairs());
+                fn = Math.addExact(fn, metrics.falseNegativePairs()); tn = Math.addExact(tn, metrics.trueNegativePairs());
+                if (metrics.f1().isPresent()) { macroF1 += metrics.f1().getAsDouble(); macroCount++; }
+                languages.add(row.language());
+            }
+            final PairwiseMetrics micro = new PairwiseMetrics(tp, fp, fn, tn);
+            text.append("| ").append(escapeMarkdown(first.stemmer())).append(TABLE_DELIMITER).append(first.processingMode())
+                    .append(TABLE_DELIMITER).append(first.outputPolicy()).append(TABLE_DELIMITER).append(languages.size())
+                    .append(TABLE_DELIMITER).append(score(micro.f05())).append(TABLE_DELIMITER).append(score(micro.f1()))
+                    .append(TABLE_DELIMITER).append(score(micro.f2())).append(TABLE_DELIMITER)
+                    .append(macroCount == 0 ? "n/a" : format(macroF1 / macroCount)).append(TABLE_DELIMITER)
+                    .append(macroCount).append(" |\n");
+        }
+        Set<String> common = null;
+        final java.util.Map<String, Set<String>> byStemmer = new java.util.TreeMap<>();
+        for (QualityResult row : rows) { byStemmer.computeIfAbsent(row.stemmer(), ignored -> new TreeSet<>()).add(row.language()); }
+        for (Set<String> supported : byStemmer.values()) {
+            if (common == null) { common = new TreeSet<>(supported); } else { common.retainAll(supported); }
+        }
+        text.append("\n### Common-language comparison\n\nCommon language intersection across displayed stemmers: ")
+                .append(common == null ? Set.of() : common).append(". Unsupported languages are not assigned zero scores.\n");
+    }
+
+    /** Converts an undefined metric to negative infinity for descending navigation order. */
+    private static double rankingValue(final QualityResult row, final String metric) { return metricValue(row, metric).orElse(Double.NEGATIVE_INFINITY); }
+
+    /** Returns a sorted defensive list for deterministic output. */
+    private static List<QualityResult> sorted(final Iterable<QualityResult> input) {
+        final List<QualityResult> rows = new ArrayList<>();
+        input.forEach(rows::add); rows.sort(QualityResult.ORDER); return rows;
+    }
+    /** Formats one human-readable ratio. */
+    private static String humanMetric(final long errors, final long possible, final OptionalDouble percentage) {
+        if (percentage.isEmpty()) { return errors + " / " + possible + " (n/a)"; }
+        return String.format(Locale.ROOT, "%d / %d (%.6f%%)", errors, possible, percentage.getAsDouble());
+    }
+    /** Formats one optional machine-readable percentage. */
+    private static String machinePercent(final OptionalDouble percentage) {
+        return percentage.isEmpty() ? "" : String.format(Locale.ROOT, "%.6f", percentage.getAsDouble());
+    }
+    /** Formats one bounded or signed score for Markdown. */
+    private static String score(final OptionalDouble value) { return value.isEmpty() ? "n/a" : format(value.getAsDouble()); }
+    /** Formats one optional score for machine-readable output. */
+    private static String machineScore(final OptionalDouble value) { return value.isEmpty() ? "" : format(value.getAsDouble()); }
+    /** Formats an unrounded calculation deterministically with scientific precision. */
+    private static String format(final double value) { return String.format(Locale.ROOT, "%.12f", value); }
+    /** Appends one correctly quoted CSV field and delimiter. */
+    private static void appendCsv(final StringBuilder output, final String value) {
+        output.append('"').append(value.replace("\"", "\"\"")).append("\",");
+    }
+    /** Escapes Markdown table delimiters. */
+    private static String escapeMarkdown(final String value) { return value.replace("|", "\\|"); }
+    /** Creates the parent directory and atomically delegates UTF-8 file writing. */
+    private static void write(final Path path, final String content) throws IOException {
+        final Path parent = path.toAbsolutePath().getParent();
+        if (parent != null) { Files.createDirectories(parent); }
+        Files.writeString(path, content, StandardCharsets.UTF_8);
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/QualityReportWriterTest.java

@@ -0,0 +1,74 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.io.IOException;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.List;
+
+import org.junit.jupiter.api.DisplayName;
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+
+/** UTF-8, formatting, ordering, escaping, and write-failure tests for reports. */
+@Tag("unit")
+@DisplayName("Stemming-quality report writer")
+final class QualityReportWriterTest {
+    /** Temporary output directory owned by JUnit. */
+    @TempDir Path temporaryDirectory;
+
+    /** Verifies required Markdown semantics and deterministic row ordering. */
+    @Test @DisplayName("Markdown contains the required English columns and deterministic metrics")
+    void markdownFormat() throws IOException {
+        final Path report = this.temporaryDirectory.resolve("report.md");
+        QualityReportWriter.writeMarkdown(report, List.of(result("Zulu", "B", 1, 2), result("Alpha|Stemmer", "A", 0, 0)), false);
+        final String text = Files.readString(report, StandardCharsets.UTF_8);
+        assertTrue(text.contains("| Stemmer | Language | Dictionary mode | Output policy | Applied dictionary rows | Processed word forms | Distinct output stems | Over-stemming | Under-stemming | Pairwise F0.5 | Pairwise F1 | Pairwise F2 |"));
+        assertTrue(text.contains("0 / 0 (n/a)"));
+        assertTrue(text.contains("1 / 2 (50.000000%)"));
+        assertTrue(text.indexOf("Alpha\\|Stemmer") < text.indexOf("Zulu"));
+        assertEquals(text, new String(Files.readAllBytes(report), StandardCharsets.UTF_8));
+    }
+
+    /** Verifies CSV headers, separate missing fields, ordering, and quoting. */
+    @Test @DisplayName("CSV uses separate English columns, correct quoting, and empty undefined percentages")
+    void csvFormat() throws IOException {
+        final Path report = this.temporaryDirectory.resolve("report.csv");
+        QualityReportWriter.writeCsv(report, List.of(result("Stemmer, \"quoted\"", "A", 0, 0)));
+        final String text = Files.readString(report, StandardCharsets.UTF_8);
+        assertTrue(text.startsWith("Stemmer,Language,Dictionary mode,Output policy,Applied dictionary rows,Processed word forms,Singleton dictionary rows,Forms with one candidate,"));
+        assertTrue(text.contains("\"Stemmer, \"\"quoted\"\"\""));
+        assertTrue(text.contains("Adjusted Rand Index,Homogeneity,Completeness,V-measure,Normalized mutual information"));
+    }
+
+    /** Verifies that filesystem failures are propagated. */
+    @Test @DisplayName("A report write failure is propagated")
+    void writeFailure() throws IOException {
+        final Path file = this.temporaryDirectory.resolve("parent-file");
+        Files.writeString(file, "occupied", StandardCharsets.UTF_8);
+        assertThrows(IOException.class, () -> QualityReportWriter.writeMarkdown(file.resolve("report.md"), List.of(), false));
+    }
+
+    /** Verifies that a second generation replaces stale content instead of appending. */
+    @Test @DisplayName("Report generation replaces stale content")
+    void reportReplacement() throws IOException {
+        final Path report = this.temporaryDirectory.resolve("replacement.md");
+        QualityReportWriter.writeMarkdown(report, List.of(result("Old", "A", 0, 1)), false);
+        QualityReportWriter.writeMarkdown(report, List.of(result("New", "B", 0, 1)), false);
+        final String text = Files.readString(report, StandardCharsets.UTF_8);
+        assertTrue(text.contains("New"));
+        assertTrue(!text.contains("Old"));
+    }
+
+    /** Creates a compact valid result for formatting tests. */
+    private static QualityResult result(final String stemmer, final String language, final long errors, final long possible) {
+        return new QualityResult(stemmer, language, ProcessingMode.ALL_WORDS, OutputPolicy.PRIMARY_OUTPUT,
+                1, 1, 1, 0, 1, 0, 1, 1, 1, errors, possible, 0, 0,
+                new PartitionMetrics(1.0, 1.0, 1.0, 1.0, 1.0));
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/QualityResult.java

@@ -0,0 +1,53 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import java.util.Comparator;
+import java.util.Objects;
+import java.util.OptionalDouble;
+
+/** Immutable pairwise stemming-quality result; all pair quantities are counts. */
+public record QualityResult(String stemmer, String language, ProcessingMode processingMode,
+        OutputPolicy outputPolicy,
+        long appliedDictionaryRows, long processedWordForms, long singletonDictionaryRows,
+        long dictionaryRowsContributingUnderPairs, long formsWithOneCandidate, long formsWithMultipleCandidates,
+        long maximumCandidatesForOneWord, long totalCandidateAssignments, long distinctOutputStems,
+        long overErrorPairs, long overPossiblePairs, long underErrorPairs, long underPossiblePairs,
+        PartitionMetrics partitionMetrics) {
+    /** Stable report ordering by stemmer, language, and processing mode. */
+    public static final Comparator<QualityResult> ORDER = Comparator.comparing(QualityResult::stemmer)
+            .thenComparing(QualityResult::language).thenComparing(QualityResult::processingMode)
+            .thenComparing(QualityResult::outputPolicy);
+
+    /** Validates non-null labels, non-negative counts, and bounded errors. */
+    public QualityResult {
+        Objects.requireNonNull(stemmer, "stemmer");
+        Objects.requireNonNull(language, "language");
+        Objects.requireNonNull(processingMode, "processingMode");
+        Objects.requireNonNull(outputPolicy, "outputPolicy");
+        final long[] counts = {appliedDictionaryRows, processedWordForms, singletonDictionaryRows,
+            dictionaryRowsContributingUnderPairs, formsWithOneCandidate, formsWithMultipleCandidates,
+            maximumCandidatesForOneWord, totalCandidateAssignments, distinctOutputStems,
+            overErrorPairs, overPossiblePairs, underErrorPairs, underPossiblePairs};
+        for (long count : counts) {
+            if (count < 0) {
+                throw new IllegalArgumentException("Quality-result counts must not be negative.");
+            }
+        }
+        if (overErrorPairs > overPossiblePairs || underErrorPairs > underPossiblePairs) {
+            throw new IllegalArgumentException("Error-pair counts must not exceed possible-pair counts.");
+        }
+        if (outputPolicy != OutputPolicy.PRIMARY_OUTPUT && partitionMetrics != null) {
+            throw new IllegalArgumentException("Partition metrics apply only to PRIMARY_OUTPUT.");
+        }
+    }
+
+    /** @return over-stemming percentage, or empty when its denominator is zero */
+    public OptionalDouble overPercentage() { return percentage(overErrorPairs, overPossiblePairs); }
+    /** @return under-stemming percentage, or empty when its denominator is zero */
+    public OptionalDouble underPercentage() { return percentage(underErrorPairs, underPossiblePairs); }
+    /** @return aggregate pairwise metrics derived from raw confusion counts */
+    public PairwiseMetrics pairwiseMetrics() { return PairwiseMetrics.from(this); }
+    /** Calculates a percentage without manufacturing a value for a zero denominator. */
+    private static OptionalDouble percentage(final long errors, final long possible) {
+        return possible == 0 ? OptionalDouble.empty() : OptionalDouble.of(100.0 * errors / possible);
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/QualityStemmerMatrixTest.java

@@ -0,0 +1,56 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.ArrayList;
+import java.util.List;
+
+import org.egothor.stemmer.benchmark.QualityStemmerMatrix;
+import org.egothor.stemmer.benchmark.QualityStemmerMatrix.Candidate;
+import org.junit.jupiter.api.DisplayName;
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+
+/** Integration checks binding report coverage to the authoritative JMH candidate registry. */
+@Tag("integration")
+@DisplayName("JMH stemming-quality candidate matrix")
+final class QualityStemmerMatrixTest {
+    /** Temporary report location. */
+    @TempDir Path temporaryDirectory;
+
+    /** Verifies discovery includes the complete current benchmark enum rather than Radixor alone. */
+    @Test @DisplayName("Candidate discovery is derived from every JMH quality candidate")
+    void discoversEveryCandidate() {
+        final List<Candidate> candidates = QualityStemmerMatrix.candidates();
+        assertEquals(92, candidates.size(), "The current adapter-language matrix size changed; report coverage must be reviewed.");
+        assertTrue(candidates.stream().anyMatch(candidate -> !candidate.name().endsWith("_RADIXOR")));
+        assertTrue(candidates.stream().anyMatch(candidate -> candidate.name().equals("DA_DK_RADIXOR")));
+        assertTrue(candidates.stream().anyMatch(candidate -> candidate.name().equals("YI_RADIXOR")));
+    }
+
+    /** Verifies a complete report row exists for both modes of every discovered candidate. */
+    @Test @DisplayName("Report rendering includes both modes for every discovered candidate")
+    void reportContainsCompleteMatrix() throws Exception {
+        final List<QualityResult> rows = new ArrayList<>();
+        for (Candidate candidate : QualityStemmerMatrix.candidates()) {
+            for (ProcessingMode mode : ProcessingMode.values()) {
+                rows.add(new QualityResult(candidate.name(), candidate.language().name(), mode,
+                        OutputPolicy.PRIMARY_OUTPUT, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0,
+                        new PartitionMetrics(1.0, 1.0, 1.0, 1.0, 1.0)));
+            }
+        }
+        final Path report = this.temporaryDirectory.resolve("matrix.csv");
+        QualityReportWriter.writeCsv(report, rows);
+        final String text = Files.readString(report, StandardCharsets.UTF_8);
+        assertEquals(185, text.lines().count());
+        for (Candidate candidate : QualityStemmerMatrix.candidates()) {
+            assertTrue(text.contains("\"" + candidate.name() + "\",\"" + candidate.language() + "\",\"ALL_WORDS\""));
+            assertTrue(text.contains("\"" + candidate.name() + "\",\"" + candidate.language() + "\",\"LOWERCASE_GROUPS_ONLY\""));
+        }
+    }
+}

src/test/java/org/egothor/stemmer/benchmark/quality/StemmerFunction.java

@@ -0,0 +1,15 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import java.io.IOException;
+
+/** Contract used to apply one production stemmer during quality evaluation. */
+@FunctionalInterface
+public interface StemmerFunction {
+    /**
+     * Stems one word form without test-specific post-processing.
+     * @param word input form, never {@code null}
+     * @return output stem, never {@code null}
+     * @throws IOException when an adapted production stemmer fails
+     */
+    String stem(String word) throws IOException;
+}

src/test/java/org/egothor/stemmer/benchmark/quality/StemmingQualityApplication.java

@@ -0,0 +1,245 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import java.io.IOException;
+import java.nio.file.Path;
+import java.util.ArrayList;
+import java.util.EnumMap;
+import java.util.EnumSet;
+import java.util.List;
+import java.util.Locale;
+import java.util.Map;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.Set;
+import java.util.logging.Level;
+import java.util.logging.Logger;
+
+import org.egothor.stemmer.StemmerPatchTrieLoader.Language;
+import org.egothor.stemmer.benchmark.QualityStemmerMatrix;
+import org.egothor.stemmer.benchmark.QualityStemmerMatrix.Candidate;
+
+/** Command-line entry point for JMH-backed pairwise stemming-quality reports. */
+public final class StemmingQualityApplication {
+    private static final int ARGUMENT_COUNT = 9;
+    private static final Logger LOGGER = Logger.getLogger(StemmingQualityApplication.class.getName());
+
+    /** Utility class. */
+    private StemmingQualityApplication() {
+        throw new AssertionError("No instances.");
+    }
+
+    /**
+     * Generates a complete report or an explicitly labelled filtered report.
+     *
+     * @param arguments output directory, language filter, candidate filter, mode
+     *                  filter, output-policy filter, audit flag, and audit contributor limit
+     * @throws IOException if dictionary, JMH adapter, or report processing fails
+     */
+    public static void main(final String[] arguments) throws IOException {
+        if (arguments.length != ARGUMENT_COUNT) {
+            throw new IllegalArgumentException("Expected output directory, resource directory, language filter, stemmer filter, dictionary-mode filter, output-policy filter, ranking metric, audit flag, and audit limit.");
+        }
+        final Path directory = Path.of(arguments[0]);
+        final LanguageUniverse universe = LanguageUniverse.discover(Path.of(arguments[1]));
+        final Set<Language> languages = parseLanguages(arguments[2]);
+        final Set<ProcessingMode> modes = parseModes(arguments[4]);
+        final Set<OutputPolicy> policies = parsePolicies(arguments[5]);
+        final String stemmerFilter = arguments[3].strip();
+        final String rankMetric = arguments[6].strip();
+        final boolean audit = Boolean.parseBoolean(arguments[7]);
+        final int auditLimit = parseAuditLimit(arguments[8]);
+        final boolean filtered = !arguments[2].isBlank() || !stemmerFilter.isBlank()
+                || !arguments[4].isBlank() || !arguments[5].isBlank();
+        final List<Candidate> candidates = selectCandidates(languages, stemmerFilter);
+        if (candidates.isEmpty()) {
+            throw new IllegalArgumentException("The supplied filters select no JMH stemming-quality candidates.");
+        }
+        if (!filtered && !languages.equals(universe.dictionaries().keySet())) {
+            throw new IllegalStateException("The complete evaluation language selection differs from the reconciled dictionary universe.");
+        }
+
+        final Map<Candidate, Boolean> multiOutput = new HashMap<>();
+        final Set<ResultKey> expected = new HashSet<>();
+        for (Candidate candidate : candidates) {
+            final boolean multiple = candidate.createStemmer().supportsMultipleOutputs();
+            multiOutput.put(candidate, multiple);
+            for (ProcessingMode mode : modes) {
+                for (OutputPolicy policy : policies) {
+                    if (policy == OutputPolicy.PRIMARY_OUTPUT || multiple) {
+                        expected.add(new ResultKey(candidate.name(), candidate.language().name(), mode, policy));
+                    }
+                }
+            }
+        }
+
+        LOGGER.log(Level.INFO, filtered ? "Starting a filtered stemming-quality report."
+                : "Starting the complete stemming-quality report.");
+        final Map<Language, List<GoldStandardGroup>> dictionaries = new EnumMap<>(Language.class);
+        final List<QualityResult> results = new ArrayList<>();
+        final List<QualityAudit.Scenario> audits = new ArrayList<>();
+        final List<CandidateQualityAudit.Scenario> candidateAudits = new ArrayList<>();
+        for (Candidate candidate : candidates) {
+            List<GoldStandardGroup> groups = dictionaries.get(candidate.language());
+            if (groups == null) {
+                groups = BundledGoldStandardLoader.load(candidate.language());
+                dictionaries.put(candidate.language(), groups);
+            }
+            for (ProcessingMode mode : modes) {
+                final QualityStemmerMatrix.BatchStemmer primaryStemmer = candidate.createStemmer();
+                final QualityResult primary;
+                if (audit && policies.contains(OutputPolicy.PRIMARY_OUTPUT)) {
+                    final QualityAudit.Scenario scenario = QualityAudit.evaluate(candidate, mode, groups, auditLimit);
+                    audits.add(scenario);
+                    primary = scenario.result();
+                } else {
+                    primary = QualityEvaluator.evaluateBatch(candidate.name(), candidate.language().name(),
+                            mode, groups, primaryStemmer);
+                }
+                if (policies.contains(OutputPolicy.PRIMARY_OUTPUT)) {
+                    results.add(primary);
+                    logScenario(candidate, mode, OutputPolicy.PRIMARY_OUTPUT);
+                }
+                if (multiOutput.get(candidate)) {
+                    final QualityResult anyCandidate = CandidateAwareEvaluator.evaluate(candidate.name(),
+                            candidate.language().name(), mode, OutputPolicy.ANY_CANDIDATE, groups, candidate.createStemmer());
+                    final QualityResult allCandidates;
+                    if (audit) {
+                        final CandidateQualityAudit.Scenario scenario = CandidateQualityAudit.evaluate(
+                                candidate, mode, groups, primary, anyCandidate, auditLimit);
+                        candidateAudits.add(scenario);
+                        allCandidates = scenario.candidate();
+                    } else {
+                        allCandidates = CandidateAwareEvaluator.evaluate(candidate.name(), candidate.language().name(),
+                                mode, OutputPolicy.ALL_CANDIDATES, groups, candidate.createStemmer());
+                    }
+                    verifyPolicyInvariants(primary, anyCandidate, allCandidates);
+                    if (policies.contains(OutputPolicy.ANY_CANDIDATE)) {
+                        results.add(anyCandidate); logScenario(candidate, mode, OutputPolicy.ANY_CANDIDATE);
+                    }
+                    if (policies.contains(OutputPolicy.ALL_CANDIDATES)) {
+                        results.add(allCandidates); logScenario(candidate, mode, OutputPolicy.ALL_CANDIDATES);
+                    }
+                }
+            }
+        }
+        validateMatrix(expected, results);
+
+        final String suffix = filtered ? "-filtered" : "";
+        final Path markdown = directory.resolve("stemming-quality" + suffix + ".md");
+        final Path csv = directory.resolve("stemming-quality" + suffix + ".csv");
+        QualityReportWriter.writeMarkdown(markdown, results, filtered, universe, candidates, expected.size(), rankMetric);
+        QualityReportWriter.writeCsv(csv, results);
+        final Path pearson = directory.resolve("metric-correlations-pearson" + suffix + ".csv");
+        final Path spearman = directory.resolve("metric-correlations-spearman" + suffix + ".csv");
+        MetricCorrelationWriter.write(pearson, spearman, results);
+        System.out.println("Stemming-quality Markdown report: " + markdown.toAbsolutePath());
+        System.out.println("Stemming-quality CSV report: " + csv.toAbsolutePath());
+        System.out.println("Pearson metric-correlation report: " + pearson.toAbsolutePath());
+        System.out.println("Spearman metric-correlation report: " + spearman.toAbsolutePath());
+        if (audit) {
+            final Path auditPath = directory.resolve("stemming-quality-audit" + suffix + ".md");
+            QualityAudit.write(auditPath, audits);
+            CandidateQualityAudit.append(auditPath, candidateAudits);
+            System.out.println("Stemming-quality audit report: " + auditPath.toAbsolutePath());
+        }
+        LOGGER.log(Level.INFO, "Completed the stemming-quality report with {0} evaluated scenarios.", results.size());
+    }
+
+    /** Selects candidates directly from the authoritative JMH matrix. */
+    private static List<Candidate> selectCandidates(final Set<Language> languages, final String filter) {
+        return QualityStemmerMatrix.candidates().stream()
+                .filter(candidate -> languages.contains(candidate.language()))
+                .filter(candidate -> filter.isBlank() || candidate.name().equalsIgnoreCase(filter)
+                        || candidate.name().toUpperCase(Locale.ROOT).endsWith("_" + filter.toUpperCase(Locale.ROOT)))
+                .toList();
+    }
+
+    /** Parses a comma-separated language filter or selects every language. */
+    private static Set<Language> parseLanguages(final String filter) {
+        if (filter.isBlank()) {
+            return EnumSet.allOf(Language.class);
+        }
+        final Set<Language> selected = EnumSet.noneOf(Language.class);
+        for (String item : filter.split(",")) {
+            selected.add(Language.valueOf(item.strip().toUpperCase(Locale.ROOT)));
+        }
+        return selected;
+    }
+
+    /** Parses a comma-separated mode filter or selects both processing modes. */
+    private static Set<ProcessingMode> parseModes(final String filter) {
+        if (filter.isBlank()) {
+            return EnumSet.allOf(ProcessingMode.class);
+        }
+        final Set<ProcessingMode> selected = EnumSet.noneOf(ProcessingMode.class);
+        for (String item : filter.split(",")) {
+            selected.add(ProcessingMode.valueOf(item.strip().toUpperCase(Locale.ROOT)));
+        }
+        return selected;
+    }
+
+    /** Parses a comma-separated output-policy filter or selects both policies. */
+    private static Set<OutputPolicy> parsePolicies(final String filter) {
+        if (filter.isBlank()) { return EnumSet.allOf(OutputPolicy.class); }
+        final Set<OutputPolicy> selected = EnumSet.noneOf(OutputPolicy.class);
+        for (String item : filter.split(",")) {
+            selected.add(OutputPolicy.valueOf(item.strip().toUpperCase(Locale.ROOT)));
+        }
+        return selected;
+    }
+
+    /** Enforces the mathematical monotonicity guaranteed by primary-output inclusion. */
+    private static void verifyPolicyInvariants(final QualityResult primary, final QualityResult any,
+            final QualityResult all) {
+        if (any.underErrorPairs() > primary.underErrorPairs() || all.underErrorPairs() > primary.underErrorPairs()
+                || any.underErrorPairs() != all.underErrorPairs() || any.overErrorPairs() > primary.overErrorPairs()
+                || all.overErrorPairs() < primary.overErrorPairs()) {
+            throw new IllegalStateException("Output-policy invariants failed for stemmer " + primary.stemmer()
+                    + ", language " + primary.language() + ", dictionary mode " + primary.processingMode()
+                    + ": PRIMARY_OUTPUT under/over=" + primary.underErrorPairs() + "/" + primary.overErrorPairs()
+                    + ", ANY_CANDIDATE under/over=" + any.underErrorPairs() + "/" + any.overErrorPairs()
+                    + ", ALL_CANDIDATES under/over=" + all.underErrorPairs() + "/" + all.overErrorPairs() + ".");
+        }
+    }
+
+    /** Validates exact expected and actual result keys, including duplicates. */
+    private static void validateMatrix(final Set<ResultKey> expected, final List<QualityResult> results) {
+        final Set<ResultKey> actual = new HashSet<>();
+        for (QualityResult result : results) {
+            final ResultKey key = ResultKey.from(result);
+            if (!actual.add(key)) { throw new IllegalStateException("Duplicate stemming-quality result key: " + key + "."); }
+        }
+        if (!expected.equals(actual)) {
+            final Set<ResultKey> missing = new HashSet<>(expected); missing.removeAll(actual);
+            final Set<ResultKey> unexpected = new HashSet<>(actual); unexpected.removeAll(expected);
+            throw new IllegalStateException("Stemming-quality result matrix mismatch. Missing rows: " + missing
+                    + "; unexpected rows: " + unexpected + ".");
+        }
+    }
+
+    /** Immutable expected-matrix key. */
+    private record ResultKey(String stemmer, String language, ProcessingMode mode, OutputPolicy policy) {
+        /** Creates a key from one immutable result. */
+        private static ResultKey from(final QualityResult result) {
+            return new ResultKey(result.stemmer(), result.language(), result.processingMode(), result.outputPolicy());
+        }
+    }
+
+    /** Parses and validates the deterministic audit contributor limit. */
+    private static int parseAuditLimit(final String value) {
+        final int limit = Integer.parseInt(value);
+        if (limit < 1) {
+            throw new IllegalArgumentException("The audit contributor limit must be positive.");
+        }
+        return limit;
+    }
+
+    /** Logs one completed scenario without per-word noise. */
+    private static void logScenario(final Candidate candidate, final ProcessingMode mode, final OutputPolicy policy) {
+        if (LOGGER.isLoggable(Level.INFO)) {
+            LOGGER.log(Level.INFO, "Completed stemming-quality evaluation for stemmer {0}, language {1}, dictionary mode {2}, and output policy {3}.",
+                    new Object[] {candidate.name(), candidate.language(), mode, policy});
+        }
+    }
+
+}

src/test/java/org/egothor/stemmer/benchmark/quality/StemmingQualityDocumentationPublisher.java

@@ -0,0 +1,743 @@
+package org.egothor.stemmer.benchmark.quality;
+
+import java.io.IOException;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.nio.file.StandardCopyOption;
+import java.security.MessageDigest;
+import java.security.NoSuchAlgorithmException;
+import java.util.ArrayList;
+import java.util.Comparator;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Locale;
+import java.util.Map;
+import java.util.Set;
+import java.util.regex.Matcher;
+import java.util.regex.Pattern;
+
+/**
+ * Publishes validated stemming-quality CSV results into marked sections of the
+ * existing language benchmark pages. This test-source utility never modifies
+ * performance benchmark content outside its markers.
+ */
+public final class StemmingQualityDocumentationPublisher {
+    private static final String START = "<!-- STEMMING-QUALITY:START -->";
+    private static final String END = "<!-- STEMMING-QUALITY:END -->";
+    private static final String OVERVIEW_START = "<!-- STEMMING-QUALITY-OVERVIEW:START -->";
+    private static final String OVERVIEW_END = "<!-- STEMMING-QUALITY-OVERVIEW:END -->";
+    private static final List<String> MODES = List.of("ALL_WORDS", "LOWERCASE_GROUPS_ONLY");
+    private static final Map<String, Integer> POLICY_ORDER = Map.of("PRIMARY_OUTPUT", 0, "ANY_CANDIDATE", 1, "ALL_CANDIDATES", 2);
+    private static final Pattern PAGE_ROW = Pattern.compile("^\\|[^|]+\\| `([^`]+)` \\| \\[([^]]+)]\\(([^)]+\\.md)\\) \\|$");
+    private static final Pattern BUILT_IN_LANGUAGE_ROW = Pattern.compile("^\\|[^|]+\\| `([^`]+)` \\|.*$");
+
+    /** Prevents construction of this command-line utility. */
+    private StemmingQualityDocumentationPublisher() { }
+
+    /**
+     * Updates or verifies the documentation from one complete source CSV.
+     *
+     * @param arguments source CSV, documentation root, and either {@code update} or {@code verify}
+     * @throws IOException when source or documentation access fails
+     */
+    public static void main(final String[] arguments) throws IOException {
+        if (arguments.length != 3) {
+            throw new IllegalArgumentException("Expected arguments: source CSV, documentation root, and update or verify mode.");
+        }
+        final Path source = Path.of(arguments[0]);
+        final Path documentationRoot = Path.of(arguments[1]);
+        final boolean update = switch (arguments[2]) {
+            case "update" -> true;
+            case "verify" -> false;
+            default -> throw new IllegalArgumentException("Documentation mode must be update or verify.");
+        };
+        publish(source, documentationRoot, update);
+    }
+
+    /**
+     * Validates the complete result set and updates or verifies every mapped page.
+     *
+     * @param source authoritative complete CSV
+     * @param documentationRoot repository documentation directory
+     * @param update whether files may be replaced
+     * @throws IOException when files cannot be read or written
+     */
+    static void publish(final Path source, final Path documentationRoot, final boolean update) throws IOException {
+        if (!Files.isRegularFile(source) || source.getFileName().toString().contains("filtered")) {
+            throw new IllegalArgumentException("The documentation source must be an existing complete, unfiltered CSV report: " + source);
+        }
+        final List<ResultRow> rows = readRows(source);
+        final Map<String, Page> pages = readPages(documentationRoot.resolve("benchmarks/languages/index.md"));
+        final Set<String> languageUniverse = readLanguageUniverse(documentationRoot.resolve("built-in-languages.md"));
+        validate(rows, pages.keySet(), languageUniverse);
+        final String checksum = sha256(source);
+        if (!update) {
+            final Path checksumFile = documentationRoot.resolve("benchmarks/data/stemming-quality.sha256");
+            final String recorded = Files.readString(checksumFile, StandardCharsets.UTF_8).strip();
+            if (!recorded.equals(checksum + "  stemming-quality.csv")) {
+                throw new IllegalStateException("The published stemming-quality checksum does not match the authoritative CSV.");
+            }
+        }
+        for (Page page : pages.values()) {
+            final List<ResultRow> languageRows = rows.stream().filter(row -> row.language().equals(page.language())).toList();
+            final String section = render(page, languageRows, checksum);
+            final Path path = documentationRoot.resolve("benchmarks/languages").resolve(page.file());
+            final String original = Files.readString(path, StandardCharsets.UTF_8);
+            final String expected = replaceSection(original, section);
+            if (update) {
+                Files.writeString(path, expected, StandardCharsets.UTF_8);
+            } else if (!original.equals(expected)) {
+                throw new IllegalStateException("Stemming-quality documentation is stale or manually altered: " + path);
+            }
+        }
+        final Path overviewPath = documentationRoot.resolve("benchmarks/index.md");
+        final String overview = Files.readString(overviewPath, StandardCharsets.UTF_8);
+        final String expectedOverview = replaceMarkedSection(overview, renderOverview(pages, rows, checksum),
+                OVERVIEW_START, OVERVIEW_END);
+        if (update) {
+            Files.writeString(overviewPath, expectedOverview, StandardCharsets.UTF_8);
+        } else if (!overview.equals(expectedOverview)) {
+            throw new IllegalStateException("The generated benchmark quality overview is stale or manually altered: " + overviewPath);
+        }
+        if (update) {
+            final Path publishedSource = documentationRoot.resolve("benchmarks/data/stemming-quality.csv");
+            Files.createDirectories(publishedSource.getParent());
+            Files.copy(source, publishedSource, StandardCopyOption.REPLACE_EXISTING);
+            Files.writeString(documentationRoot.resolve("benchmarks/data/stemming-quality.sha256"), checksum + "  stemming-quality.csv\n", StandardCharsets.UTF_8);
+        }
+        System.out.printf(Locale.ROOT, "%s stemming-quality documentation for %d languages from %d validated rows.%n",
+                update ? "Updated" : "Verified", pages.size(), rows.stream().filter(row -> pages.containsKey(row.language())).count());
+    }
+
+    /** Reads the authoritative built-in language identifiers from the existing registry table. */
+    private static Set<String> readLanguageUniverse(final Path builtInLanguages) throws IOException {
+        final Set<String> languages = new HashSet<>();
+        for (String line : Files.readAllLines(builtInLanguages, StandardCharsets.UTF_8)) {
+            final Matcher matcher = BUILT_IN_LANGUAGE_ROW.matcher(line);
+            if (matcher.matches()) {
+                languages.add(matcher.group(1));
+            }
+        }
+        if (languages.isEmpty()) {
+            throw new IllegalStateException("No authoritative built-in languages were discovered in " + builtInLanguages);
+        }
+        return Set.copyOf(languages);
+    }
+
+    /** Reads the language-code-to-page mapping from the existing documentation index. */
+    private static Map<String, Page> readPages(final Path index) throws IOException {
+        final Map<String, Page> pages = new LinkedHashMap<>();
+        for (String line : Files.readAllLines(index, StandardCharsets.UTF_8)) {
+            final Matcher matcher = PAGE_ROW.matcher(line);
+            if (matcher.matches()) {
+                final Page previous = pages.put(matcher.group(1), new Page(matcher.group(1), matcher.group(2), matcher.group(3)));
+                if (previous != null) {
+                    throw new IllegalStateException("Duplicate language mapping in benchmark index: " + matcher.group(1));
+                }
+            }
+        }
+        if (pages.isEmpty()) {
+            throw new IllegalStateException("No language benchmark pages were discovered in " + index);
+        }
+        return pages;
+    }
+
+    /** Reads and schema-validates the quoted UTF-8 CSV. */
+    private static List<ResultRow> readRows(final Path source) throws IOException {
+        final List<String> lines = Files.readAllLines(source, StandardCharsets.UTF_8);
+        if (lines.isEmpty()) {
+            throw new IllegalStateException("The stemming-quality CSV is empty.");
+        }
+        final List<String> header = parseCsv(lines.getFirst());
+        final List<String> required = List.of("Stemmer", "Language", "Dictionary mode", "Output policy", "Applied dictionary rows",
+                "Processed word forms", "Forms with multiple candidates", "Maximum candidates for one form", "Total candidate assignments",
+                "True-positive pairs", "False-positive pairs", "False-negative pairs", "True-negative pairs",
+                "Over-stemming error pairs", "Over-stemming possible pairs", "Over-stemming percentage", "Under-stemming error pairs",
+                "Under-stemming possible pairs", "Under-stemming percentage", "Pairwise precision", "Pairwise recall", "Pairwise specificity",
+                "Pairwise accuracy", "Balanced accuracy", "Pairwise F0.5", "Pairwise F1", "Pairwise F2", "Jaccard index",
+                "Fowlkes-Mallows index", "Matthews correlation coefficient", "Pairwise error rate", "Adjusted Rand Index", "Homogeneity",
+                "Completeness", "V-measure", "Normalized mutual information");
+        if (!header.containsAll(required)) {
+            throw new IllegalStateException("The stemming-quality CSV does not contain the required publication schema.");
+        }
+        final Map<String, Integer> indexes = new HashMap<>();
+        for (int index = 0; index < header.size(); index++) {
+            indexes.put(header.get(index), index);
+        }
+        final List<ResultRow> rows = new ArrayList<>();
+        for (int line = 1; line < lines.size(); line++) {
+            final List<String> values = parseCsv(lines.get(line));
+            if (values.size() != header.size()) {
+                throw new IllegalStateException("CSV column count differs from the header at logical row " + (line + 1));
+            }
+            rows.add(new ResultRow(values, indexes));
+        }
+        return List.copyOf(rows);
+    }
+
+    /** Parses one RFC-4180-compatible line emitted by the quality report writer. */
+    private static List<String> parseCsv(final String line) {
+        final List<String> values = new ArrayList<>();
+        final StringBuilder value = new StringBuilder();
+        boolean quoted = false;
+        for (int index = 0; index < line.length(); index++) {
+            final char character = line.charAt(index);
+            if (character == '"') {
+                if (quoted && index + 1 < line.length() && line.charAt(index + 1) == '"') {
+                    value.append('"');
+                    index++;
+                } else {
+                    quoted = !quoted;
+                }
+            } else if (character == ',' && !quoted) {
+                values.add(value.toString());
+                value.setLength(0);
+            } else {
+                value.append(character);
+            }
+        }
+        if (quoted) {
+            throw new IllegalStateException("Unterminated quoted CSV value.");
+        }
+        values.add(value.toString());
+        return values;
+    }
+
+    /** Validates uniqueness, coverage, raw arithmetic, metrics, and policy invariants. */
+    private static void validate(final List<ResultRow> rows, final Set<String> documentedLanguages,
+            final Set<String> languageUniverse) {
+        final Set<String> keys = new HashSet<>();
+        for (ResultRow row : rows) {
+            if (!keys.add(row.key())) {
+                throw new IllegalStateException("Duplicate stemming-quality result key: " + row.key());
+            }
+            row.validate();
+        }
+        final Set<String> resultLanguages = new HashSet<>();
+        rows.forEach(row -> resultLanguages.add(row.language()));
+        if (!resultLanguages.equals(languageUniverse)) {
+            throw new IllegalStateException("Complete-report language coverage differs from the authoritative built-in universe. Results: "
+                    + resultLanguages + "; authoritative languages: " + languageUniverse);
+        }
+        for (String language : languageUniverse) {
+            for (String mode : MODES) {
+                for (String policy : POLICY_ORDER.keySet()) {
+                    final boolean present = rows.stream().anyMatch(row -> row.language().equals(language) && row.mode().equals(mode)
+                            && row.policy().equals(policy) && row.stemmer().endsWith("_RADIXOR"));
+                    if (!present) {
+                        throw new IllegalStateException("The complete report omits Radixor result " + language + "/" + mode + "/" + policy);
+                    }
+                }
+            }
+        }
+        for (String language : documentedLanguages) {
+            final List<ResultRow> languageRows = rows.stream().filter(row -> row.language().equals(language)).toList();
+            if (languageRows.isEmpty()) {
+                throw new IllegalStateException("No stemming-quality results exist for documented language " + language);
+            }
+            for (String mode : MODES) {
+                if (languageRows.stream().noneMatch(row -> row.mode().equals(mode))) {
+                    throw new IllegalStateException("Missing dictionary mode " + mode + " for documented language " + language);
+                }
+            }
+            validatePolicies(languageRows);
+        }
+        if (!documentedLanguages.contains("DA_DK") || !documentedLanguages.contains("YI")) {
+            throw new IllegalStateException("The documentation mapping must contain DA_DK and YI.");
+        }
+    }
+
+    /** Validates policy monotonicity for each multi-output scenario. */
+    private static void validatePolicies(final List<ResultRow> rows) {
+        final Map<String, Map<String, ResultRow>> scenarios = new HashMap<>();
+        for (ResultRow row : rows) {
+            scenarios.computeIfAbsent(row.stemmer() + "\u0000" + row.mode(), ignored -> new HashMap<>()).put(row.policy(), row);
+        }
+        for (Map<String, ResultRow> policies : scenarios.values()) {
+            final ResultRow primary = policies.get("PRIMARY_OUTPUT");
+            if (primary == null) {
+                throw new IllegalStateException("Every documented stemmer scenario must contain PRIMARY_OUTPUT.");
+            }
+            if (policies.containsKey("ANY_CANDIDATE") || policies.containsKey("ALL_CANDIDATES")) {
+                final ResultRow any = policies.get("ANY_CANDIDATE");
+                final ResultRow all = policies.get("ALL_CANDIDATES");
+                if (any == null || all == null || any.fn() > primary.fn() || all.fn() != any.fn()
+                        || any.fp() > primary.fp() || all.fp() < primary.fp()) {
+                    throw new IllegalStateException("Output-policy invariants fail for " + primary.key());
+                }
+            }
+        }
+    }
+
+    /** Renders one complete generated section for a language page. */
+    private static String render(final Page page, final List<ResultRow> rows, final String checksum) {
+        final StringBuilder output = new StringBuilder(32768);
+        output.append(START).append("\n\n## Stemming Quality\n\n")
+                .append("Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `")
+                .append(page.language()).append("` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.\n\n")
+                .append("`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).\n\n")
+                .append("### Evaluation Scope and Key Findings\n\n")
+                .append("The dictionary resource is `src/main/resources/").append(page.language().toLowerCase(Locale.ROOT)).append("/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.\n\n");
+        for (String mode : MODES) {
+            appendFinding(output, rows, mode);
+        }
+        for (String mode : MODES) {
+            final List<ResultRow> selected = rows.stream().filter(row -> row.mode().equals(mode)).sorted(resultOrder()).toList();
+            final long stemmers = selected.stream().map(ResultRow::stemmer).distinct().count();
+            final long policies = selected.stream().map(ResultRow::policy).distinct().count();
+            output.append("### `").append(mode).append("`\n\n")
+                    .append("This mode contains **").append(selected.size()).append(" result rows**, **").append(stemmers)
+                    .append(" evaluated stemmers**, and **").append(policies).append(" output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.\n\n");
+            for (String policy : List.of("PRIMARY_OUTPUT", "ANY_CANDIDATE", "ALL_CANDIDATES")) {
+                final List<ResultRow> policyRows = selected.stream().filter(row -> row.policy().equals(policy)).toList();
+                if (!policyRows.isEmpty()) {
+                    output.append("#### `").append(policy).append("` ranking\n\n");
+                    renderPrimaryTable(output, policyRows);
+                    renderDetailedTables(output, policyRows);
+                }
+            }
+            renderCandidateAnalysis(output, selected);
+        }
+        appendMethodology(output);
+        output.append("### Provenance\n\n")
+                .append("- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`\n")
+                .append("- Source SHA-256: `").append(checksum).append("`\n")
+                .append("- Evaluation command: `./gradlew stemmingQuality`\n")
+                .append("- Dictionary language: `").append(page.language()).append("`\n")
+                .append("- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`\n")
+                .append("- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`\n")
+                .append("- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV\n\n")
+                .append(END).append('\n');
+        return output.toString();
+    }
+
+    /** Appends one deterministic primary-output winner and runner-up statement. */
+    private static void appendFinding(final StringBuilder output, final List<ResultRow> rows, final String mode) {
+        final List<ResultRow> primary = rows.stream().filter(row -> row.mode().equals(mode) && row.policy().equals("PRIMARY_OUTPUT"))
+                .sorted(resultOrder()).toList();
+        final ResultRow winner = primary.getFirst();
+        final ResultRow runnerUp = primary.size() > 1 ? primary.get(1) : null;
+        output.append("- **").append(mode).append(":** `").append(displayStemmer(winner.stemmer())).append("` ranks first by balanced accuracy at **")
+                .append(metric(winner, "Balanced accuracy")).append("** among ").append(primary.size()).append(" deterministic stemmers");
+        if (runnerUp == null) {
+            output.append("; no same-language competitor was available");
+        } else {
+            final double difference = winner.number("Balanced accuracy") - runnerUp.number("Balanced accuracy");
+            output.append(". The runner-up is `").append(displayStemmer(runnerUp.stemmer())).append("` at ")
+                    .append(metric(runnerUp, "Balanced accuracy")).append(", a difference of ")
+                    .append(String.format(Locale.ROOT, "%.6f", difference));
+            if (difference == 0.0) {
+                output.append(" (an exact tie before formatting)");
+            }
+        }
+        output.append(". This rank does not imply leadership in throughput or every secondary metric.\n");
+    }
+
+    /** Renders the compact primary ranking table in an accessible scroll region. */
+    private static void renderPrimaryTable(final StringBuilder output, final List<ResultRow> rows) {
+        output.append("<div class=\"quality-table quality-table--compact\" role=\"region\" aria-label=\"Compact stemming-quality ranking; scroll horizontally for additional columns\" tabindex=\"0\" markdown=\"1\">\n\n")
+                .append("| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |\n")
+                .append("|---:|---|---|---:|---:|---:|---:|---:|---:|\n");
+        for (int index = 0; index < rows.size(); index++) {
+            final ResultRow row = rows.get(index);
+            output.append('|').append(index + 1).append('|').append(displayStemmer(row.stemmer())).append('|').append(row.policy()).append('|')
+                    .append(metric(row, "Balanced accuracy")).append('|')
+                    .append(pair(row, "Over-stemming error pairs", "Over-stemming possible pairs", "Over-stemming percentage")).append('|')
+                    .append(pair(row, "Under-stemming error pairs", "Under-stemming possible pairs", "Under-stemming percentage")).append('|')
+                    .append(metric(row, "Pairwise F0.5")).append('|').append(metric(row, "Pairwise F1")).append('|')
+                    .append(metric(row, "Matthews correlation coefficient")).append("|\n");
+        }
+        output.append("\n</div>\n\n");
+    }
+
+    /** Renders classification, relation, partition, and raw-count tables with repeated identities. */
+    private static void renderDetailedTables(final StringBuilder output, final List<ResultRow> rows) {
+        output.append("<details class=\"quality-details\" markdown=\"1\"><summary>Classification metrics</summary>\n\n")
+                .append("| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |\n")
+                .append("|---:|---|---|---:|---:|---:|---:|---:|---:|\n");
+        for (int index = 0; index < rows.size(); index++) {
+            final ResultRow row = rows.get(index);
+            output.append(identity(index, row)).append(metric(row, "Pairwise precision")).append('|').append(metric(row, "Pairwise recall")).append('|')
+                    .append(metric(row, "Pairwise specificity")).append('|').append(metric(row, "Balanced accuracy")).append('|')
+                    .append(metric(row, "Pairwise accuracy")).append('|').append(metric(row, "Pairwise error rate")).append("|\n");
+        }
+        output.append("\n</details>\n\n<details class=\"quality-details\" markdown=\"1\"><summary>Pair-relation metrics</summary>\n\n")
+                .append("| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | Fowlkes–Mallows | MCC |\n")
+                .append("|---:|---|---|---:|---:|---:|---:|---:|---:|\n");
+        for (int index = 0; index < rows.size(); index++) {
+            final ResultRow row = rows.get(index);
+            output.append(identity(index, row)).append(metric(row, "Pairwise F0.5")).append('|').append(metric(row, "Pairwise F1")).append('|')
+                    .append(metric(row, "Pairwise F2")).append('|').append(metric(row, "Jaccard index")).append('|')
+                    .append(metric(row, "Fowlkes-Mallows index")).append('|').append(metric(row, "Matthews correlation coefficient")).append("|\n");
+        }
+        output.append("\n</details>\n\n<details class=\"quality-details\" markdown=\"1\"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>\n\n")
+                .append("| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |\n")
+                .append("|---:|---|---|---:|---:|---:|---:|---:|\n");
+        for (int index = 0; index < rows.size(); index++) {
+            final ResultRow row = rows.get(index);
+            output.append(identity(index, row)).append(metric(row, "Adjusted Rand Index")).append('|').append(metric(row, "Homogeneity")).append('|')
+                    .append(metric(row, "Completeness")).append('|').append(metric(row, "V-measure")).append('|')
+                    .append(metric(row, "Normalized mutual information")).append("|\n");
+        }
+        output.append("\n</details>\n\n<details class=\"quality-details\" markdown=\"1\"><summary>Raw pair counts</summary>\n\n")
+                .append("| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |\n")
+                .append("|---:|---|---|---:|---:|---:|---:|---:|---:|\n");
+        for (int index = 0; index < rows.size(); index++) {
+            final ResultRow row = rows.get(index);
+            output.append(identity(index, row)).append(row.value("True-positive pairs")).append('|').append(row.value("False-positive pairs"))
+                    .append('|').append(row.value("False-negative pairs")).append('|').append(row.value("True-negative pairs")).append('|')
+                    .append(row.value("Over-stemming error pairs")).append(" / ").append(row.value("Over-stemming possible pairs")).append('|')
+                    .append(row.value("Under-stemming error pairs")).append(" / ").append(row.value("Under-stemming possible pairs")).append("|\n");
+        }
+        output.append("\n</details>\n\n");
+    }
+
+    /** Renders the candidate-policy trade-off for every genuinely multi-output adapter. */
+    private static void renderCandidateAnalysis(final StringBuilder output, final List<ResultRow> rows) {
+        final Map<String, Map<String, ResultRow>> byStemmer = new LinkedHashMap<>();
+        rows.forEach(row -> byStemmer.computeIfAbsent(row.stemmer(), ignored -> new HashMap<>()).put(row.policy(), row));
+        final List<Map.Entry<String, Map<String, ResultRow>>> multi = byStemmer.entrySet().stream()
+                .filter(entry -> entry.getValue().containsKey("ANY_CANDIDATE")).sorted(Map.Entry.comparingByKey()).toList();
+        if (multi.isEmpty()) {
+            return;
+        }
+        output.append("#### Multi-output analysis\n\nAlternative candidates are capability analyses, not replacements for the deterministic comparison.\n\n")
+                .append("| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |\n")
+                .append("|---|---:|---:|---:|---:|---:|---:|---:|\n");
+        for (Map.Entry<String, Map<String, ResultRow>> entry : multi) {
+            final ResultRow primary = entry.getValue().get("PRIMARY_OUTPUT");
+            final ResultRow any = entry.getValue().get("ANY_CANDIDATE");
+            final ResultRow all = entry.getValue().get("ALL_CANDIDATES");
+            final long forms = any.longValue("Processed word forms");
+            final long multiple = any.longValue("Forms with multiple candidates");
+            output.append('|').append(displayStemmer(entry.getKey())).append('|').append(primary.fn() - any.fn()).append('|')
+                    .append(primary.fp() - any.fp()).append('|').append(all.fp() - primary.fp()).append('|').append(multiple).append('|')
+                    .append(String.format(Locale.ROOT, "%.6f%%", 100.0 * multiple / forms)).append('|')
+                    .append(any.value("Maximum candidates for one form")).append('|').append(any.value("Total candidate assignments")).append("|\n");
+        }
+        output.append('\n');
+    }
+
+    /** Returns the repeated rank, stemmer, and policy prefix for a detailed table row. */
+    private static String identity(final int index, final ResultRow row) {
+        return "|" + (index + 1) + "|" + displayStemmer(row.stemmer()) + "|" + row.policy() + "|";
+    }
+
+    /** Converts authoritative adapter identifiers into a stable readable label without merging competitors. */
+    private static String displayStemmer(final String identifier) {
+        return identifier.endsWith("_RADIXOR") ? "Radixor" : identifier.replace('_', ' ');
+    }
+
+    /** Appends the self-contained policy, confusion-matrix, and metric definitions. */
+    private static void appendMethodology(final StringBuilder output) {
+        output.append("### Output Policies and Metric Definitions\n\n")
+                .append("`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.\n\n")
+                .append("For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.\n\n")
+                .append("- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.\n")
+                .append("- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.\n")
+                .append("- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.\n")
+                .append("- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.\n")
+                .append("- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.\n")
+                .append("- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.\n")
+                .append("- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.\n")
+                .append("- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.\n")
+                .append("- Jaccard index: `TP / (TP + FP + FN)`.\n")
+                .append("- Fowlkes–Mallows index: `sqrt(precision * recall)`.\n")
+                .append("- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.\n")
+                .append("- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.\n\n")
+                .append("Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.\n\n");
+    }
+
+    /** Renders the generated executive findings, winner matrix, and Radixor aggregates. */
+    private static String renderOverview(final Map<String, Page> pages, final List<ResultRow> rows, final String checksum) {
+        final StringBuilder output = new StringBuilder(16384);
+        output.append(OVERVIEW_START).append("\n\n## Pairwise Quality Findings\n\n")
+                .append("The validated snapshot is a broad multilingual comparison covering the complete 20-language Radixor dictionary universe; 19 languages have existing benchmark pages. The direct ranking below uses only deterministic `PRIMARY_OUTPUT` rows over identical per-language inputs. Candidate-aware rows are intentionally excluded from this claim.\n\n");
+        int radixorWins = 0;
+        int comparisons = 0;
+        for (String mode : MODES) {
+            for (String language : pages.keySet()) {
+                final List<ResultRow> ranked = primaryRows(rows, language, mode);
+                comparisons++;
+                if (ranked.getFirst().stemmer().endsWith("_RADIXOR")) {
+                    radixorWins++;
+                }
+            }
+        }
+        if (radixorWins == comparisons) {
+            output.append("!!! success \"Evidence-based primary-output result\"\n    Radixor achieved the highest balanced accuracy among the evaluated deterministic stemmers for every documented language in both `ALL_WORDS` and `LOWERCASE_GROUPS_ONLY`: **")
+                    .append(radixorWins).append(" wins in ").append(comparisons).append(" language-mode comparisons, with no exact first-place ties**. This statement is limited to the evaluated implementations, versions, dictionaries, adapters, and balanced-accuracy metric; it is not a universal claim about every stemming use case.\n\n");
+        } else {
+            output.append("Radixor ranks first in **").append(radixorWins).append(" of ").append(comparisons)
+                    .append("** documented primary-output language-mode comparisons.\n\n");
+        }
+        output.append("### Per-language winner matrix\n\n| Language | Dictionary mode | Winner | Balanced accuracy | Runner-up | Difference | Exact tie | Deterministic stemmers |\n")
+                .append("|---|---|---|---:|---|---:|---|---:|\n");
+        for (Page page : pages.values()) {
+            for (String mode : MODES) {
+                final List<ResultRow> ranked = primaryRows(rows, page.language(), mode);
+                final ResultRow winner = ranked.getFirst();
+                final ResultRow runner = ranked.size() > 1 ? ranked.get(1) : null;
+                final double difference = runner == null ? Double.NaN : winner.number("Balanced accuracy") - runner.number("Balanced accuracy");
+                output.append('|').append(page.displayName()).append(" (`").append(page.language()).append("`)|").append(mode).append('|')
+                        .append(displayStemmer(winner.stemmer())).append('|').append(metric(winner, "Balanced accuracy")).append('|')
+                        .append(runner == null ? "n/a" : displayStemmer(runner.stemmer())).append('|')
+                        .append(runner == null ? "n/a" : String.format(Locale.ROOT, "%.9f", difference)).append('|')
+                        .append(runner != null && difference == 0.0 ? "yes" : "no").append('|').append(ranked.size()).append("|\n");
+            }
+        }
+        renderSecondaryLeaders(output, pages, rows);
+        output.append("\n### Win, tie, and placement summary\n\nCounts use `PRIMARY_OUTPUT` only and retain each adapter configuration as a separate stemmer except that language-specific Radixor identifiers are combined as Radixor. Coverage is displayed explicitly; unsupported languages are absent, not losses.\n\n");
+        for (String mode : MODES) {
+            renderPlacementSummary(output, pages, rows, mode);
+        }
+        output.append("\n### Radixor full-coverage aggregates\n\nThese aggregates cover all 19 documented languages. Macro balanced accuracy gives each language equal weight. Micro metrics first sum raw pair counts across languages. Unsupported third-party languages are never inserted as zero results, so this full-coverage table is not presented as a cross-stemmer common-language ranking.\n\n")
+                .append("| Dictionary mode | Languages | Macro balanced accuracy | Micro balanced accuracy | Micro precision | Micro recall | Micro F1 |\n")
+                .append("|---|---:|---:|---:|---:|---:|---:|\n");
+        for (String mode : MODES) {
+            final List<ResultRow> radixor = rows.stream().filter(row -> pages.containsKey(row.language()) && row.mode().equals(mode)
+                    && row.policy().equals("PRIMARY_OUTPUT") && row.stemmer().endsWith("_RADIXOR")).toList();
+            final double macroBalanced = radixor.stream().mapToDouble(row -> row.number("Balanced accuracy")).average().orElseThrow();
+            long tp = 0;
+            long fp = 0;
+            long fn = 0;
+            long tn = 0;
+            for (ResultRow row : radixor) {
+                tp = Math.addExact(tp, row.longValue("True-positive pairs"));
+                fp = Math.addExact(fp, row.fp());
+                fn = Math.addExact(fn, row.fn());
+                tn = Math.addExact(tn, row.longValue("True-negative pairs"));
+            }
+            final double precision = (double) tp / Math.addExact(tp, fp);
+            final double recall = (double) tp / Math.addExact(tp, fn);
+            final double specificity = (double) tn / Math.addExact(tn, fp);
+            final double f1 = 2.0 * tp / (2.0 * tp + fp + fn);
+            output.append('|').append(mode).append('|').append(radixor.size()).append('|').append(format(macroBalanced)).append('|')
+                    .append(format((recall + specificity) / 2.0)).append('|').append(format(precision)).append('|')
+                    .append(format(recall)).append('|').append(format(f1)).append("|\n");
+        }
+        output.append("\n### Reproducible data\n\n- [Machine-readable quality snapshot](data/stemming-quality.csv)\n")
+                .append("- SHA-256: `").append(checksum).append("`\n")
+                .append("- [Linguistic quality methodology](reference/linguistic-quality.md)\n")
+                .append("- [Tested stemmer inventory](reference/tested-stemmers.md)\n")
+                .append("- [Reproducibility and raw data](reference/reproducibility.md)\n")
+                .append("- Pearson and Spearman correlation files are generated under `build/reports/stemming-quality/`; they are separated by dictionary mode and output policy. Correlation does not establish metric equivalence.\n\n")
+                .append(OVERVIEW_END).append('\n');
+        return output.toString();
+    }
+
+    /** Publishes every deterministic secondary-metric case led by a non-Radixor adapter. */
+    private static void renderSecondaryLeaders(final StringBuilder output, final Map<String, Page> pages,
+            final List<ResultRow> rows) {
+        final Map<String, Boolean> metrics = new LinkedHashMap<>();
+        metrics.put("Pairwise precision", true);
+        metrics.put("Pairwise recall", true);
+        metrics.put("Pairwise F0.5", true);
+        metrics.put("Pairwise F1", true);
+        metrics.put("Pairwise F2", true);
+        metrics.put("Matthews correlation coefficient", true);
+        metrics.put("Over-stemming percentage", false);
+        metrics.put("Under-stemming percentage", false);
+        final StringBuilder cases = new StringBuilder();
+        int count = 0;
+        for (Page page : pages.values()) {
+            for (String mode : MODES) {
+                final List<ResultRow> primary = primaryRows(rows, page.language(), mode);
+                for (Map.Entry<String, Boolean> metric : metrics.entrySet()) {
+                    final Comparator<ResultRow> comparator = Comparator.comparingDouble(row -> row.number(metric.getKey()));
+                    final ResultRow leader = metric.getValue() ? primary.stream().max(comparator).orElseThrow()
+                            : primary.stream().min(comparator).orElseThrow();
+                    if (!leader.stemmer().endsWith("_RADIXOR")) {
+                        count++;
+                        cases.append('|').append(page.displayName()).append('|').append(mode).append('|').append(metric.getKey()).append('|')
+                                .append(displayStemmer(leader.stemmer())).append('|').append(metric(leader, metric.getKey())).append("|\n");
+                    }
+                }
+            }
+        }
+        output.append("\n### Secondary-metric trade-offs\n\nBalanced-accuracy leadership does not imply leadership on every error trade-off. The table below lists all **")
+                .append(count).append("** deterministic primary-output language-mode-metric cases where a non-Radixor adapter has the best displayed value. Equal values are resolved by the authoritative row ordering and should be read as ties when the unrounded values are equal. Throughput leadership remains in the separate performance tables.\n\n")
+                .append("<details class=\"quality-details\" markdown=\"1\"><summary>Non-Radixor secondary-metric leaders</summary>\n\n")
+                .append("| Language | Dictionary mode | Metric | Leader | Value |\n|---|---|---|---|---:|\n")
+                .append(cases).append("\n</details>\n");
+    }
+
+    /** Renders coverage-aware placement statistics for one dictionary mode. */
+    private static void renderPlacementSummary(final StringBuilder output, final Map<String, Page> pages,
+            final List<ResultRow> rows, final String mode) {
+        final Map<String, List<Integer>> ranks = new HashMap<>();
+        final Map<String, Integer> wins = new HashMap<>();
+        final Map<String, Integer> ties = new HashMap<>();
+        final Map<String, Integer> topThree = new HashMap<>();
+        for (String language : pages.keySet()) {
+            final List<ResultRow> ranked = primaryRows(rows, language, mode);
+            final double leading = ranked.getFirst().number("Balanced accuracy");
+            final long leaders = ranked.stream().filter(row -> row.number("Balanced accuracy") == leading).count();
+            for (int index = 0; index < ranked.size(); index++) {
+                final ResultRow row = ranked.get(index);
+                final String name = displayStemmer(row.stemmer());
+                ranks.computeIfAbsent(name, ignored -> new ArrayList<>()).add(index + 1);
+                if (row.number("Balanced accuracy") == leading) {
+                    wins.merge(name, 1, Integer::sum);
+                    if (leaders > 1) {
+                        ties.merge(name, 1, Integer::sum);
+                    }
+                }
+                if (index < 3) {
+                    topThree.merge(name, 1, Integer::sum);
+                }
+            }
+        }
+        output.append("<details class=\"quality-details\" markdown=\"1\"><summary>").append(mode).append(" placements</summary>\n\n")
+                .append("| Stemmer | Evaluated languages | Wins | Exact first-place ties | Top-three placements | Average rank | Median rank |\n")
+                .append("|---|---:|---:|---:|---:|---:|---:|\n");
+        final List<String> names = ranks.keySet().stream().sorted(Comparator
+                .comparingInt((String name) -> wins.getOrDefault(name, 0)).reversed()
+                .thenComparing(Comparator.comparingInt((String name) -> ranks.get(name).size()).reversed())
+                .thenComparing(name -> name)).toList();
+        for (String name : names) {
+            final List<Integer> placements = ranks.get(name).stream().sorted().toList();
+            final double average = placements.stream().mapToInt(Integer::intValue).average().orElseThrow();
+            final int middle = placements.size() / 2;
+            final double median = placements.size() % 2 == 0
+                    ? (placements.get(middle - 1) + placements.get(middle)) / 2.0 : placements.get(middle);
+            output.append('|').append(name).append('|').append(placements.size()).append('|').append(wins.getOrDefault(name, 0)).append('|')
+                    .append(ties.getOrDefault(name, 0)).append('|').append(topThree.getOrDefault(name, 0)).append('|')
+                    .append(String.format(Locale.ROOT, "%.3f", average)).append('|').append(String.format(Locale.ROOT, "%.3f", median)).append("|\n");
+        }
+        output.append("\n</details>\n\n");
+    }
+
+    /** Returns deterministically ranked primary-output rows for one language and mode. */
+    private static List<ResultRow> primaryRows(final List<ResultRow> rows, final String language, final String mode) {
+        return rows.stream().filter(row -> row.language().equals(language) && row.mode().equals(mode)
+                && row.policy().equals("PRIMARY_OUTPUT")).sorted(resultOrder()).toList();
+    }
+
+    /** Formats an aggregate metric at the publication precision. */
+    private static String format(final double value) {
+        return String.format(Locale.ROOT, "%.6f", value);
+    }
+
+    /** Returns the deterministic publication order based on unrounded source values. */
+    private static Comparator<ResultRow> resultOrder() {
+        return Comparator.comparingDouble((ResultRow row) -> row.number("Balanced accuracy")).reversed()
+                .thenComparing(Comparator.comparingDouble((ResultRow row) -> row.number("Matthews correlation coefficient")).reversed())
+                .thenComparing(Comparator.comparingDouble((ResultRow row) -> row.number("Pairwise F1")).reversed())
+                .thenComparingDouble(row -> row.number("Over-stemming percentage"))
+                .thenComparingLong(row -> row.longValue("Over-stemming error pairs"))
+                .thenComparingDouble(row -> row.number("Under-stemming percentage"))
+                .thenComparing(ResultRow::stemmer).thenComparingInt(row -> POLICY_ORDER.get(row.policy()));
+    }
+
+    /** Formats a score to the publication-wide six-decimal precision. */
+    private static String metric(final ResultRow row, final String name) {
+        final String value = row.value(name);
+        return value.isEmpty() ? "n/a" : String.format(Locale.ROOT, "%.6f", Double.parseDouble(value));
+    }
+
+    /** Formats one raw error numerator, denominator, and percentage. */
+    private static String pair(final ResultRow row, final String error, final String possible, final String percentage) {
+        final String rate = row.value(percentage);
+        return row.value(error) + " / " + row.value(possible) + " (" + (rate.isEmpty() ? "n/a" : String.format(Locale.ROOT, "%.6f%%", Double.parseDouble(rate))) + ")";
+    }
+
+    /** Replaces an existing marked section or appends the first generated section. */
+    private static String replaceSection(final String original, final String section) {
+        return replaceMarkedSection(original, section, START, END);
+    }
+
+    /** Replaces or appends a section delimited by the supplied deterministic markers. */
+    private static String replaceMarkedSection(final String original, final String section, final String startMarker,
+            final String endMarker) {
+        final int start = original.indexOf(startMarker);
+        final int end = original.indexOf(endMarker);
+        if ((start < 0) != (end < 0) || (start >= 0 && end < start)) {
+            throw new IllegalStateException("Malformed stemming-quality generated-section markers.");
+        }
+        if (start < 0) {
+            return original.stripTrailing() + "\n\n" + section;
+        }
+        return original.substring(0, start) + section + original.substring(end + endMarker.length()).stripLeading();
+    }
+
+    /** Calculates a lowercase hexadecimal SHA-256 checksum. */
+    private static String sha256(final Path source) throws IOException {
+        try {
+            final byte[] digest = MessageDigest.getInstance("SHA-256").digest(Files.readAllBytes(source));
+            final StringBuilder text = new StringBuilder(digest.length * 2);
+            for (byte value : digest) {
+                text.append(String.format(Locale.ROOT, "%02x", value & 0xff));
+            }
+            return text.toString();
+        } catch (NoSuchAlgorithmException exception) {
+            throw new IllegalStateException("The required SHA-256 algorithm is unavailable.", exception);
+        }
+    }
+
+    /** Immutable mapping from a language identifier to its existing page. */
+    private record Page(String language, String displayName, String file) { }
+
+    /** Immutable view of one authoritative CSV row. */
+    private record ResultRow(List<String> values, Map<String, Integer> indexes) {
+        /** Creates and validates an immutable row view. */
+        private ResultRow {
+            values = List.copyOf(values);
+            indexes = Map.copyOf(indexes);
+        }
+
+        /** Returns a field by its exact English header. */
+        private String value(final String name) { return this.values.get(this.indexes.get(name)); }
+        /** Returns the stemmer identifier. */
+        private String stemmer() { return value("Stemmer"); }
+        /** Returns the language identifier. */
+        private String language() { return value("Language"); }
+        /** Returns the dictionary-processing mode. */
+        private String mode() { return value("Dictionary mode"); }
+        /** Returns the output policy. */
+        private String policy() { return value("Output policy"); }
+        /** Returns a unique scenario key. */
+        private String key() { return stemmer() + "/" + language() + "/" + mode() + "/" + policy(); }
+        /** Parses a required long field. */
+        private long longValue(final String name) { return Long.parseLong(value(name)); }
+        /** Parses a numeric field, placing undefined values last during sorting. */
+        private double number(final String name) { return value(name).isEmpty() ? Double.NEGATIVE_INFINITY : Double.parseDouble(value(name)); }
+        /** Returns false-negative pairs. */
+        private long fn() { return longValue("False-negative pairs"); }
+        /** Returns false-positive pairs. */
+        private long fp() { return longValue("False-positive pairs"); }
+
+        /** Validates raw confusion counts and the published balanced accuracy. */
+        private void validate() {
+            final long tp = longValue("True-positive pairs");
+            final long fp = fp();
+            final long fn = fn();
+            final long tn = longValue("True-negative pairs");
+            if (fn != longValue("Under-stemming error pairs") || fp != longValue("Over-stemming error pairs")
+                    || Math.addExact(tp, fn) != longValue("Under-stemming possible pairs")
+                    || Math.addExact(tn, fp) != longValue("Over-stemming possible pairs")) {
+                throw new IllegalStateException("Raw pair-count invariants fail for " + key());
+            }
+            final double recall = ratio(tp, Math.addExact(tp, fn));
+            final double specificity = ratio(tn, Math.addExact(tn, fp));
+            final double expected = (recall + specificity) / 2.0;
+            if (Math.abs(expected - number("Balanced accuracy")) > 0.0000000000015) {
+                throw new IllegalStateException("Balanced accuracy is inconsistent with raw counts for " + key());
+            }
+            if (!policy().equals("PRIMARY_OUTPUT") && !value("Adjusted Rand Index").isEmpty()) {
+                throw new IllegalStateException("Partition-only metrics are present for a candidate relation: " + key());
+            }
+        }
+
+        /** Divides raw counts with explicit zero-denominator handling. */
+        private static double ratio(final long numerator, final long denominator) {
+            if (denominator == 0) {
+                throw new IllegalStateException("A balanced-accuracy component is undefined in a published result row.");
+            }
+            return (double) numerator / (double) denominator;
+        }
+    }
+}