{"abstract":"An uncertainty belongs to the wrong sensor channel.","category":"Sensor fusion consistency","checks":6,"contract":"Given a 2x2 measurement covariance in source order and a permutation of [0,1], return covariance in requested channel order.","contract_signature":"covariance, order","evaluation_group":"model-a9d73f19963c4ec5","failed_approach":"Permuting rows alone breaks diagonal association and symmetry.","family":"z-sensor_fusion-channel-covariance-order","id":"FA-12066","implementations":{"attempt":{"sha256":"a86a64fdfd6c7b018070bbe54f5337903b19e17d109a168f7a0e4912d17d7c05","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(covariance, order):\n    return [covariance[i] for i in order]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('unequal uncertainties', solve([[N,0],[0,4*N]],[1,0]), [[4*N,0],[0,N]])\ncheck('cross covariance', solve([[2*N,N],[N,3*N]],[1,0]), [[3*N,N],[N,2*N]])\ncheck('negative cross covariance', solve([[2*N,-N],[-N,3*N]],[1,0]), [[3*N,-N],[-N,2*N]])\ncheck('identity channel order', solve([[N,0],[0,2*N]],[0,1]), [[N,0],[0,2*N]])\ncheck('equal variances', solve([[2*N,N],[N,2*N]],[1,0]), [[2*N,N],[N,2*N]])\ncheck('zero covariance boundary', solve([[0,0],[0,N]],[1,0]), [[N,0],[0,0]])\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":"8d1d79a22b6bda7331864f2bf33381a1b1ff1766f72b14398a1cb089c1eda5dc","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(covariance, order):\n    return covariance\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('unequal uncertainties', solve([[N,0],[0,4*N]],[1,0]), [[4*N,0],[0,N]])\ncheck('cross covariance', solve([[2*N,N],[N,3*N]],[1,0]), [[3*N,N],[N,2*N]])\ncheck('negative cross covariance', solve([[2*N,-N],[-N,3*N]],[1,0]), [[3*N,-N],[-N,2*N]])\ncheck('identity channel order', solve([[N,0],[0,2*N]],[0,1]), [[N,0],[0,2*N]])\ncheck('equal variances', solve([[2*N,N],[N,2*N]],[1,0]), [[2*N,N],[N,2*N]])\ncheck('zero covariance boundary', solve([[0,0],[0,N]],[1,0]), [[N,0],[0,0]])\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-channel-covariance-order","generated_at":"2026-09-29T14:38:53.573488+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":"Only the measurement vector is reordered.","sha256":"34591fd9509da9f3ae19eb3ad85a607a0d161eadc50822c03a5f45b0739b034b","title":"Measurement channel permutation leaves covariance behind · 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":41.979,"exit_code":1,"observations":[{"actual":[[0,4],[1,0]],"check":"unequal uncertainties","expected":[[4,0],[0,1]],"passed":false},{"actual":[[1,3],[2,1]],"check":"cross covariance","expected":[[3,1],[1,2]],"passed":false},{"actual":[[-1,3],[2,-1]],"check":"negative cross covariance","expected":[[3,-1],[-1,2]],"passed":false},{"actual":[[1,0],[0,2]],"check":"identity channel order","expected":[[1,0],[0,2]],"passed":true},{"actual":[[1,2],[2,1]],"check":"equal variances","expected":[[2,1],[1,2]],"passed":false},{"actual":[[0,1],[0,0]],"check":"zero covariance boundary","expected":[[1,0],[0,0]],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"unequal uncertainties\", \"actual\": [[0, 4], [1, 0]], \"expected\": [[4, 0], [0, 1]], \"passed\": false}, {\"check\": \"cross covariance\", \"actual\": [[1, 3], [2, 1]], \"expected\": [[3, 1], [1, 2]], \"passed\": false}, {\"check\": \"negative cross covariance\", \"actual\": [[-1, 3], [2, -1]], \"expected\": [[3, -1], [-1, 2]], \"passed\": false}, {\"check\": \"identity channel order\", \"actual\": [[1, 0], [0, 2]], \"expected\": [[1, 0], [0, 2]], \"passed\": true}, {\"check\": \"equal variances\", \"actual\": [[1, 2], [2, 1]], \"expected\": [[2, 1], [1, 2]], \"passed\": false}, {\"check\": \"zero covariance boundary\", \"actual\": [[0, 1], [0, 0]], \"expected\": [[1, 0], [0, 0]], \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":44.03,"exit_code":1,"observations":[{"actual":[[1,0],[0,4]],"check":"unequal uncertainties","expected":[[4,0],[0,1]],"passed":false},{"actual":[[2,1],[1,3]],"check":"cross covariance","expected":[[3,1],[1,2]],"passed":false},{"actual":[[2,-1],[-1,3]],"check":"negative cross covariance","expected":[[3,-1],[-1,2]],"passed":false},{"actual":[[1,0],[0,2]],"check":"identity channel order","expected":[[1,0],[0,2]],"passed":true},{"actual":[[2,1],[1,2]],"check":"equal variances","expected":[[2,1],[1,2]],"passed":true},{"actual":[[0,0],[0,1]],"check":"zero covariance boundary","expected":[[1,0],[0,0]],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"unequal uncertainties\", \"actual\": [[1, 0], [0, 4]], \"expected\": [[4, 0], [0, 1]], \"passed\": false}, {\"check\": \"cross covariance\", \"actual\": [[2, 1], [1, 3]], \"expected\": [[3, 1], [1, 2]], \"passed\": false}, {\"check\": \"negative cross covariance\", \"actual\": [[2, -1], [-1, 3]], \"expected\": [[3, -1], [-1, 2]], \"passed\": false}, {\"check\": \"identity channel order\", \"actual\": [[1, 0], [0, 2]], \"expected\": [[1, 0], [0, 2]], \"passed\": true}, {\"check\": \"equal variances\", \"actual\": [[2, 1], [1, 2]], \"expected\": [[2, 1], [1, 2]], \"passed\": true}, {\"check\": \"zero covariance boundary\", \"actual\": [[0, 0], [0, 1]], \"expected\": [[1, 0], [0, 0]], \"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."}}