{"abstract":"The reduction disagrees with its explicit aggregation oracle.","category":"Numerical aggregation","checks":8,"contract":"Expand nonnegative integer frequencies. Remove exactly k lowest and k highest observations by multiplicity. Return the exact mean as a Fraction string, or None when no observations survive.","contract_signature":"rows, k","evaluation_group":"s3-na-frequency-symmetric-trim-mean","failed_approach":"Subtracting only one tail count leaves the denominator too large.","family":"s3-numerical-aggregation-frequency-symmetric-trim-mean-trim-original-denominator","id":"FA-13306","implementations":{"attempt":{"sha256":"4e6569a9dc806604b79b4e28fa2cb2a06933933eaf3873dc6258559cc4ed1a0a","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nfrom collections import Counter, defaultdict\nimport math\nimport itertools\nN = 1\nobservations = []\ndef solve(rows, k):\n    xs=sorted(x for x,w in rows for _ in range(w))\n    if not xs or 2*k>=len(xs): return None\n    return str(Fraction(sum(xs[k:len(xs)-k]),len(xs)-k))\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([(9, 1), (1, 4), (5, 2)], 2)), '7/3')\ncheck('regression 2', solve(*([(2, 4)], 1)), '2')\ncheck('regression 3', solve(*([], 0)), None)\ncheck('regression 4', solve(*([(1, 2), (4, 2)], 2)), None)\ncheck('regression 5', solve(*([(0, 1), (10, 3), (-5, 2)], 1)), '15/4')\ncheck('regression 6', solve(*([(3, 0), (8, 1)], 0)), '8')\ncheck('regression 7', solve(*([(1, 1), (8, 4), (9, 1)], 1)), '8')\ncheck(\"variable weighted trim\",solve([(N,3),(N+6,2)],1),str(Fraction(3*N+6,3)))\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":"139dfd961ab209e617ecf0c65de1a95fffe0a3e60f2ba9a287e1bb82c6a792b0","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nfrom collections import Counter, defaultdict\nimport math\nimport itertools\nN = 1\nobservations = []\ndef solve(rows, k):\n    xs=sorted(x for x,w in rows for _ in range(w))\n    if not xs or 2*k>=len(xs): return None\n    return str(Fraction(sum(xs[k:len(xs)-k]),len(xs)))\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('regression 1', solve(*([(9, 1), (1, 4), (5, 2)], 2)), '7/3')\ncheck('regression 2', solve(*([(2, 4)], 1)), '2')\ncheck('regression 3', solve(*([], 0)), None)\ncheck('regression 4', solve(*([(1, 2), (4, 2)], 2)), None)\ncheck('regression 5', solve(*([(0, 1), (10, 3), (-5, 2)], 1)), '15/4')\ncheck('regression 6', solve(*([(3, 0), (8, 1)], 0)), '8')\ncheck('regression 7', solve(*([(1, 1), (8, 4), (9, 1)], 1)), '8')\ncheck(\"variable weighted trim\",solve([(N,3),(N+6,2)],1),str(Fraction(3*N+6,3)))\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":"Small offline integer/rational inputs only; no performance, statistical inference, or production-library conformance claim. 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":"s3-numerical-aggregation-frequency-symmetric-trim-mean-trim-original-denominator","generated_at":"2026-09-29T14:39:06.052066+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Exact bounded examples isolate a reduction defect without floating-point or external-service effects.","root_cause":"The trimmed total is divided by the original population size.","sha256":"e9eb21b44a822ed54b99adeb9489022fb5b75a19166b223aef574f75c4888954","title":"Frequency symmetric trim mean: The trimmed total is divided by the original population size. · 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":48.403,"exit_code":1,"observations":[{"actual":"7/5","check":"regression 1","expected":"7/3","passed":false},{"actual":"4/3","check":"regression 2","expected":"2","passed":false},{"actual":null,"check":"regression 3","expected":null,"passed":true},{"actual":null,"check":"regression 4","expected":null,"passed":true},{"actual":"3","check":"regression 5","expected":"15/4","passed":false},{"actual":"8","check":"regression 6","expected":"8","passed":true},{"actual":"32/5","check":"regression 7","expected":"8","passed":false},{"actual":"9/4","check":"variable weighted trim","expected":"3","passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": \"7/5\", \"expected\": \"7/3\", \"passed\": false}, {\"check\": \"regression 2\", \"actual\": \"4/3\", \"expected\": \"2\", \"passed\": false}, {\"check\": \"regression 3\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression 4\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression 5\", \"actual\": \"3\", \"expected\": \"15/4\", \"passed\": false}, {\"check\": \"regression 6\", \"actual\": \"8\", \"expected\": \"8\", \"passed\": true}, {\"check\": \"regression 7\", \"actual\": \"32/5\", \"expected\": \"8\", \"passed\": false}, {\"check\": \"variable weighted trim\", \"actual\": \"9/4\", \"expected\": \"3\", \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":47.953,"exit_code":1,"observations":[{"actual":"1","check":"regression 1","expected":"7/3","passed":false},{"actual":"1","check":"regression 2","expected":"2","passed":false},{"actual":null,"check":"regression 3","expected":null,"passed":true},{"actual":null,"check":"regression 4","expected":null,"passed":true},{"actual":"5/2","check":"regression 5","expected":"15/4","passed":false},{"actual":"8","check":"regression 6","expected":"8","passed":true},{"actual":"16/3","check":"regression 7","expected":"8","passed":false},{"actual":"9/5","check":"variable weighted trim","expected":"3","passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 1\", \"actual\": \"1\", \"expected\": \"7/3\", \"passed\": false}, {\"check\": \"regression 2\", \"actual\": \"1\", \"expected\": \"2\", \"passed\": false}, {\"check\": \"regression 3\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression 4\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression 5\", \"actual\": \"5/2\", \"expected\": \"15/4\", \"passed\": false}, {\"check\": \"regression 6\", \"actual\": \"8\", \"expected\": \"8\", \"passed\": true}, {\"check\": \"regression 7\", \"actual\": \"16/3\", \"expected\": \"8\", \"passed\": false}, {\"check\": \"variable weighted trim\", \"actual\": \"9/5\", \"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."}}