{"abstract":"Prediction confidence remains too high after a long acquisition gap.","category":"Sensor fusion consistency","checks":6,"contract":"Scalar random walk with variance rate q: return p+q*dt as an integer; p,q,dt nonnegative. This is diffusion, not acceleration noise.","contract_signature":"p, q, dt","evaluation_group":"model-7c1952807b66257b","failed_approach":"Squaring elapsed time applies a constant-velocity uncertainty law to random-walk diffusion.","family":"z-sensor_fusion-elapsed-process-noise","id":"FA-12076","implementations":{"attempt":{"sha256":"2577ace4cb505f6f718d570f76934338395d66aa44d88c8bc50e2b39a1174638","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(p, q, dt):\n    return p+q*dt*dt\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('gap spans several ticks', solve(N,2,N+2), 3*N+4)\ncheck('zero elapsed time', solve(N,2,0), N)\ncheck('one tick', solve(N,2,1), N+2)\ncheck('no diffusion', solve(N,0,N+2), N)\ncheck('zero prior variance', solve(0,N,3), 3*N)\ncheck('two ticks', solve(N,N,2), 3*N)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"110688e2a534ef44a0a63637cc5338b7e4256069cdf243f2280b02206728907e","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(p, q, dt):\n    return p+q\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('gap spans several ticks', solve(N,2,N+2), 3*N+4)\ncheck('zero elapsed time', solve(N,2,0), N)\ncheck('one tick', solve(N,2,1), N+2)\ncheck('no diffusion', solve(N,0,N+2), N)\ncheck('zero prior variance', solve(0,N,3), 3*N)\ncheck('two ticks', solve(N,N,2), 3*N)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"Exact small scalar or two-axis models; no nonlinear dynamics, numerical conditioning, or real sensor noise simulation. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"z-sensor_fusion-elapsed-process-noise","generated_at":"2026-09-29T14:38:53.619448+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Deterministic sensor-fusion model isolating one consistency contract; no hardware or production estimator is simulated.","root_cause":"One process-noise increment is added per delivery instead of elapsed time.","sha256":"81c60ccb59d4f70b506a6e884875e6ebb21f3255dda7fde761374a0a612d3e47","title":"Dropped samples do not accumulate process uncertainty · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":42.089,"exit_code":1,"observations":[{"actual":19,"check":"gap spans several ticks","expected":7,"passed":false},{"actual":1,"check":"zero elapsed time","expected":1,"passed":true},{"actual":3,"check":"one tick","expected":3,"passed":true},{"actual":1,"check":"no diffusion","expected":1,"passed":true},{"actual":9,"check":"zero prior variance","expected":3,"passed":false},{"actual":5,"check":"two ticks","expected":3,"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"gap spans several ticks\", \"actual\": 19, \"expected\": 7, \"passed\": false}, {\"check\": \"zero elapsed time\", \"actual\": 1, \"expected\": 1, \"passed\": true}, {\"check\": \"one tick\", \"actual\": 3, \"expected\": 3, \"passed\": true}, {\"check\": \"no diffusion\", \"actual\": 1, \"expected\": 1, \"passed\": true}, {\"check\": \"zero prior variance\", \"actual\": 9, \"expected\": 3, \"passed\": false}, {\"check\": \"two ticks\", \"actual\": 5, \"expected\": 3, \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":41.632,"exit_code":1,"observations":[{"actual":3,"check":"gap spans several ticks","expected":7,"passed":false},{"actual":3,"check":"zero elapsed time","expected":1,"passed":false},{"actual":3,"check":"one tick","expected":3,"passed":true},{"actual":1,"check":"no diffusion","expected":1,"passed":true},{"actual":1,"check":"zero prior variance","expected":3,"passed":false},{"actual":2,"check":"two ticks","expected":3,"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"gap spans several ticks\", \"actual\": 3, \"expected\": 7, \"passed\": false}, {\"check\": \"zero elapsed time\", \"actual\": 3, \"expected\": 1, \"passed\": false}, {\"check\": \"one tick\", \"actual\": 3, \"expected\": 3, \"passed\": true}, {\"check\": \"no diffusion\", \"actual\": 1, \"expected\": 1, \"passed\": true}, {\"check\": \"zero prior variance\", \"actual\": 1, \"expected\": 3, \"passed\": false}, {\"check\": \"two ticks\", \"actual\": 2, \"expected\": 3, \"passed\": false}], \"passed\": false}\n"}},"member_only":{"stages":["fixed"],"fields":["implementations.fixed","verification.fixed","harness","repair"],"note":"The verified repair, its recorded checks, the repair description, and the scoring harness are available to members."}}