{"abstract":"Overshoot is measured from the initial density instead of carrying capacity.","category":"Ecological population dynamics","checks":7,"contract":"hist[0]=N0; N[t+1] = max(0, N[t] + r*N[t]*(1 - N[t-lag]/K)) where values before time 0 equal N0; return [history rounded 4, overshoot max(0, max(history)-K) rounded 4].","contract_signature":"n0, r, k, lag, steps","evaluation_group":"w2-ecopop-delayed-logistic","failed_approach":"Excluding the initial value ignores a start above capacity.","family":"w2-ecopop-delayed-logistic-overshoot-reference","id":"FA-65746","implementations":{"attempt":{"sha256":"5f3c80f049f2b40009ba9e6d8dd7f19d43b4134426902cee608f7814717a252a","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(n0, r, k, lag, steps):\n    hist = [float(n0)]\n    for t in range(steps):\n        past = hist[t - lag] if t - lag >= 0 else hist[0]\n        now = hist[t]\n        hist.append(max(0.0, now + r * now * (1 - past / k)))\n    over = max(0.0, max(hist[1:] or [0.0]) - k)\n    return [[round(x, 4) for x in hist], round(over, 4)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: daphnia with 2 step lag',\n   (10, 0.8, 100, 2, 8),\n   [[10.0, 17.2, 29.584, 50.8845, 84.5904, 132.2425, 184.2037, 206.9118, 153.541], 106.9118]),\n  ('regression: no lag',\n   (10, 0.5, 100, 0, 6),\n   [[10.0, 14.5, 20.6988, 28.9059, 39.1811, 51.0959, 63.5899], 0.0]),\n  ('regression: long lag oscillation',\n   (50, 0.6, 100, 3, 10),\n   [[50.0, 65.0, 84.5, 109.85, 142.805, 172.794, 188.8639, 177.702, 132.0628, 74.3825, 34.723], 88.8639]),\n  ('regression: start above capacity',\n   (150, 0.3, 100, 1, 5),\n   [[150.0, 127.5, 108.375, 99.4341, 96.9358, 97.1004], 50.0]),\n  ('regression: one-step lag',\n   (20, 1.2, 200, 1, 7),\n   [[20.0, 41.6, 86.528, 168.7642, 283.6643, 336.8272, 167.7447, 30.0325], 136.8272]),\n  ('control: zero steps', (30, 0.5, 100, 2, 0), [[30.0], 0.0]),\n  ('regression: crash to zero', (180, 2.5, 100, 1, 4), [[180.0, 0.0, 0.0, 0.0, 0.0], 80.0])],\n [('regression: daphnia with 2 step lag',\n   (10, 0.8, 100, 2, 8),\n   [[10.0, 17.2, 29.584, 50.8845, 84.5904, 132.2425, 184.2037, 206.9118, 153.541], 106.9118]),\n  ('regression: no lag',\n   (10, 0.5, 100, 0, 6),\n   [[10.0, 14.5, 20.6988, 28.9059, 39.1811, 51.0959, 63.5899], 0.0]),\n  ('regression: long lag oscillation',\n   (50, 0.6, 100, 3, 10),\n   [[50.0, 65.0, 84.5, 109.85, 142.805, 172.794, 188.8639, 177.702, 132.0628, 74.3825, 34.723], 88.8639]),\n  ('regression: start above capacity',\n   (150, 0.3, 100, 1, 5),\n   [[150.0, 127.5, 108.375, 99.4341, 96.9358, 97.1004], 50.0]),\n  ('regression: one-step lag',\n   (20, 1.2, 200, 1, 7),\n   [[20.0, 41.6, 86.528, 168.7642, 283.6643, 336.8272, 167.7447, 30.0325], 136.8272]),\n  ('control: zero steps', (30, 0.5, 100, 2, 0), [[30.0], 0.0]),\n  ('regression: crash to zero', (180, 2.5, 100, 1, 4), [[180.0, 0.0, 0.0, 0.0, 0.0], 80.0])],\n [('regression: daphnia with 2 step lag',\n   (10, 0.8, 100, 2, 8),\n   [[10.0, 17.2, 29.584, 50.8845, 84.5904, 132.2425, 184.2037, 206.9118, 153.541], 106.9118]),\n  ('regression: no lag',\n   (10, 0.5, 100, 0, 6),\n   [[10.0, 14.5, 20.6988, 28.9059, 39.1811, 51.0959, 63.5899], 0.0]),\n  ('regression: long lag oscillation',\n   (50, 0.6, 100, 3, 10),\n   [[50.0, 65.0, 84.5, 109.85, 142.805, 172.794, 188.8639, 177.702, 132.0628, 74.3825, 34.723], 88.8639]),\n  ('regression: start above capacity',\n   (150, 0.3, 100, 1, 5),\n   [[150.0, 127.5, 108.375, 99.4341, 96.9358, 97.1004], 50.0]),\n  ('regression: one-step lag',\n   (20, 1.2, 200, 1, 7),\n   [[20.0, 41.6, 86.528, 168.7642, 283.6643, 336.8272, 167.7447, 30.0325], 136.8272]),\n  ('control: zero steps', (30, 0.5, 100, 2, 0), [[30.0], 0.0]),\n  ('regression: crash to zero', (180, 2.5, 100, 1, 4), [[180.0, 0.0, 0.0, 0.0, 0.0], 80.0])],\n [('regression: daphnia with 2 step lag',\n   (10, 0.8, 100, 2, 8),\n   [[10.0, 17.2, 29.584, 50.8845, 84.5904, 132.2425, 184.2037, 206.9118, 153.541], 106.9118]),\n  ('regression: no lag',\n   (10, 0.5, 100, 0, 6),\n   [[10.0, 14.5, 20.6988, 28.9059, 39.1811, 51.0959, 63.5899], 0.0]),\n  ('regression: long lag oscillation',\n   (50, 0.6, 100, 3, 10),\n   [[50.0, 65.0, 84.5, 109.85, 142.805, 172.794, 188.8639, 177.702, 132.0628, 74.3825, 34.723], 88.8639]),\n  ('regression: start above capacity',\n   (150, 0.3, 100, 1, 5),\n   [[150.0, 127.5, 108.375, 99.4341, 96.9358, 97.1004], 50.0]),\n  ('regression: one-step lag',\n   (20, 1.2, 200, 1, 7),\n   [[20.0, 41.6, 86.528, 168.7642, 283.6643, 336.8272, 167.7447, 30.0325], 136.8272]),\n  ('control: zero steps', (30, 0.5, 100, 2, 0), [[30.0], 0.0]),\n  ('regression: crash to zero', (180, 2.5, 100, 1, 4), [[180.0, 0.0, 0.0, 0.0, 0.0], 80.0])],\n [('regression: daphnia with 2 step lag',\n   (10, 0.8, 100, 2, 8),\n   [[10.0, 17.2, 29.584, 50.8845, 84.5904, 132.2425, 184.2037, 206.9118, 153.541], 106.9118]),\n  ('regression: no lag',\n   (10, 0.5, 100, 0, 6),\n   [[10.0, 14.5, 20.6988, 28.9059, 39.1811, 51.0959, 63.5899], 0.0]),\n  ('regression: long lag oscillation',\n   (50, 0.6, 100, 3, 10),\n   [[50.0, 65.0, 84.5, 109.85, 142.805, 172.794, 188.8639, 177.702, 132.0628, 74.3825, 34.723], 88.8639]),\n  ('regression: start above capacity',\n   (150, 0.3, 100, 1, 5),\n   [[150.0, 127.5, 108.375, 99.4341, 96.9358, 97.1004], 50.0]),\n  ('regression: one-step lag',\n   (20, 1.2, 200, 1, 7),\n   [[20.0, 41.6, 86.528, 168.7642, 283.6643, 336.8272, 167.7447, 30.0325], 136.8272]),\n  ('control: zero steps', (30, 0.5, 100, 2, 0), [[30.0], 0.0]),\n  ('regression: crash to zero', (180, 2.5, 100, 1, 4), [[180.0, 0.0, 0.0, 0.0, 0.0], 80.0])]]\nfor label, args, expected in fixtures[N - 1]:\n    check(label, solve(*args), expected)\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":"9ef37a044815214947bb998d056aae88a65a19641264b7dec312d6c1d6c58c17","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(n0, r, k, lag, steps):\n    hist = [float(n0)]\n    for t in range(steps):\n        past = hist[t - lag] if t - lag >= 0 else hist[0]\n        now = hist[t]\n        hist.append(max(0.0, now + r * now * (1 - past / k)))\n    over = max(0.0, max(hist) - hist[0])\n    return [[round(x, 4) for x in hist], round(over, 4)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: daphnia with 2 step lag',\n   (10, 0.8, 100, 2, 8),\n   [[10.0, 17.2, 29.584, 50.8845, 84.5904, 132.2425, 184.2037, 206.9118, 153.541], 106.9118]),\n  ('regression: no lag',\n   (10, 0.5, 100, 0, 6),\n   [[10.0, 14.5, 20.6988, 28.9059, 39.1811, 51.0959, 63.5899], 0.0]),\n  ('regression: long lag oscillation',\n   (50, 0.6, 100, 3, 10),\n   [[50.0, 65.0, 84.5, 109.85, 142.805, 172.794, 188.8639, 177.702, 132.0628, 74.3825, 34.723], 88.8639]),\n  ('regression: start above capacity',\n   (150, 0.3, 100, 1, 5),\n   [[150.0, 127.5, 108.375, 99.4341, 96.9358, 97.1004], 50.0]),\n  ('regression: one-step lag',\n   (20, 1.2, 200, 1, 7),\n   [[20.0, 41.6, 86.528, 168.7642, 283.6643, 336.8272, 167.7447, 30.0325], 136.8272]),\n  ('control: zero steps', (30, 0.5, 100, 2, 0), [[30.0], 0.0]),\n  ('regression: crash to zero', (180, 2.5, 100, 1, 4), [[180.0, 0.0, 0.0, 0.0, 0.0], 80.0])],\n [('regression: daphnia with 2 step lag',\n   (10, 0.8, 100, 2, 8),\n   [[10.0, 17.2, 29.584, 50.8845, 84.5904, 132.2425, 184.2037, 206.9118, 153.541], 106.9118]),\n  ('regression: no lag',\n   (10, 0.5, 100, 0, 6),\n   [[10.0, 14.5, 20.6988, 28.9059, 39.1811, 51.0959, 63.5899], 0.0]),\n  ('regression: long lag oscillation',\n   (50, 0.6, 100, 3, 10),\n   [[50.0, 65.0, 84.5, 109.85, 142.805, 172.794, 188.8639, 177.702, 132.0628, 74.3825, 34.723], 88.8639]),\n  ('regression: start above capacity',\n   (150, 0.3, 100, 1, 5),\n   [[150.0, 127.5, 108.375, 99.4341, 96.9358, 97.1004], 50.0]),\n  ('regression: one-step lag',\n   (20, 1.2, 200, 1, 7),\n   [[20.0, 41.6, 86.528, 168.7642, 283.6643, 336.8272, 167.7447, 30.0325], 136.8272]),\n  ('control: zero steps', (30, 0.5, 100, 2, 0), [[30.0], 0.0]),\n  ('regression: crash to zero', (180, 2.5, 100, 1, 4), [[180.0, 0.0, 0.0, 0.0, 0.0], 80.0])],\n [('regression: daphnia with 2 step lag',\n   (10, 0.8, 100, 2, 8),\n   [[10.0, 17.2, 29.584, 50.8845, 84.5904, 132.2425, 184.2037, 206.9118, 153.541], 106.9118]),\n  ('regression: no lag',\n   (10, 0.5, 100, 0, 6),\n   [[10.0, 14.5, 20.6988, 28.9059, 39.1811, 51.0959, 63.5899], 0.0]),\n  ('regression: long lag oscillation',\n   (50, 0.6, 100, 3, 10),\n   [[50.0, 65.0, 84.5, 109.85, 142.805, 172.794, 188.8639, 177.702, 132.0628, 74.3825, 34.723], 88.8639]),\n  ('regression: start above capacity',\n   (150, 0.3, 100, 1, 5),\n   [[150.0, 127.5, 108.375, 99.4341, 96.9358, 97.1004], 50.0]),\n  ('regression: one-step lag',\n   (20, 1.2, 200, 1, 7),\n   [[20.0, 41.6, 86.528, 168.7642, 283.6643, 336.8272, 167.7447, 30.0325], 136.8272]),\n  ('control: zero steps', (30, 0.5, 100, 2, 0), [[30.0], 0.0]),\n  ('regression: crash to zero', (180, 2.5, 100, 1, 4), [[180.0, 0.0, 0.0, 0.0, 0.0], 80.0])],\n [('regression: daphnia with 2 step lag',\n   (10, 0.8, 100, 2, 8),\n   [[10.0, 17.2, 29.584, 50.8845, 84.5904, 132.2425, 184.2037, 206.9118, 153.541], 106.9118]),\n  ('regression: no lag',\n   (10, 0.5, 100, 0, 6),\n   [[10.0, 14.5, 20.6988, 28.9059, 39.1811, 51.0959, 63.5899], 0.0]),\n  ('regression: long lag oscillation',\n   (50, 0.6, 100, 3, 10),\n   [[50.0, 65.0, 84.5, 109.85, 142.805, 172.794, 188.8639, 177.702, 132.0628, 74.3825, 34.723], 88.8639]),\n  ('regression: start above capacity',\n   (150, 0.3, 100, 1, 5),\n   [[150.0, 127.5, 108.375, 99.4341, 96.9358, 97.1004], 50.0]),\n  ('regression: one-step lag',\n   (20, 1.2, 200, 1, 7),\n   [[20.0, 41.6, 86.528, 168.7642, 283.6643, 336.8272, 167.7447, 30.0325], 136.8272]),\n  ('control: zero steps', (30, 0.5, 100, 2, 0), [[30.0], 0.0]),\n  ('regression: crash to zero', (180, 2.5, 100, 1, 4), [[180.0, 0.0, 0.0, 0.0, 0.0], 80.0])],\n [('regression: daphnia with 2 step lag',\n   (10, 0.8, 100, 2, 8),\n   [[10.0, 17.2, 29.584, 50.8845, 84.5904, 132.2425, 184.2037, 206.9118, 153.541], 106.9118]),\n  ('regression: no lag',\n   (10, 0.5, 100, 0, 6),\n   [[10.0, 14.5, 20.6988, 28.9059, 39.1811, 51.0959, 63.5899], 0.0]),\n  ('regression: long lag oscillation',\n   (50, 0.6, 100, 3, 10),\n   [[50.0, 65.0, 84.5, 109.85, 142.805, 172.794, 188.8639, 177.702, 132.0628, 74.3825, 34.723], 88.8639]),\n  ('regression: start above capacity',\n   (150, 0.3, 100, 1, 5),\n   [[150.0, 127.5, 108.375, 99.4341, 96.9358, 97.1004], 50.0]),\n  ('regression: one-step lag',\n   (20, 1.2, 200, 1, 7),\n   [[20.0, 41.6, 86.528, 168.7642, 283.6643, 336.8272, 167.7447, 30.0325], 136.8272]),\n  ('control: zero steps', (30, 0.5, 100, 2, 0), [[30.0], 0.0]),\n  ('regression: crash to zero', (180, 2.5, 100, 1, 4), [[180.0, 0.0, 0.0, 0.0, 0.0], 80.0])]]\nfor label, args, expected in fixtures[N - 1]:\n    check(label, solve(*args), expected)\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":"Deterministic bounded teaching model with a stipulated contract; not a validated scientific or public-health modelling library. 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":"w2-ecopop-delayed-logistic-overshoot-reference","generated_at":"2026-09-29T14:47:36.745626+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Population projections set harvest quotas, conservation status and pest-control timing; a wrong update order, boundary or rate conversion silently changes management advice.","root_cause":"The reference level is N0.","sha256":"8a16e0e1324d005bddd5ca535e5d9fe2afe82e3ab103274a46518b1f51a9ab51","title":"Discrete delayed logistic (Hutchinson) with lag: overshoot reference · 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":41.717,"exit_code":1,"observations":[{"actual":[[10.0,17.2,29.584,50.8845,84.5904,132.2425,184.2037,206.9118,153.541],106.9118],"check":"regression: daphnia with 2 step lag","expected":[[10.0,17.2,29.584,50.8845,84.5904,132.2425,184.2037,206.9118,153.541],106.9118],"passed":true},{"actual":[[10.0,14.5,20.6988,28.9059,39.1811,51.0959,63.5899],0.0],"check":"regression: no lag","expected":[[10.0,14.5,20.6988,28.9059,39.1811,51.0959,63.5899],0.0],"passed":true},{"actual":[[50.0,65.0,84.5,109.85,142.805,172.794,188.8639,177.702,132.0628,74.3825,34.723],88.8639],"check":"regression: long lag oscillation","expected":[[50.0,65.0,84.5,109.85,142.805,172.794,188.8639,177.702,132.0628,74.3825,34.723],88.8639],"passed":true},{"actual":[[150.0,127.5,108.375,99.4341,96.9358,97.1004],27.5],"check":"regression: start above capacity","expected":[[150.0,127.5,108.375,99.4341,96.9358,97.1004],50.0],"passed":false},{"actual":[[20.0,41.6,86.528,168.7642,283.6643,336.8272,167.7447,30.0325],136.8272],"check":"regression: one-step lag","expected":[[20.0,41.6,86.528,168.7642,283.6643,336.8272,167.7447,30.0325],136.8272],"passed":true},{"actual":[[30.0],0.0],"check":"control: zero steps","expected":[[30.0],0.0],"passed":true},{"actual":[[180.0,0.0,0.0,0.0,0.0],0.0],"check":"regression: crash to zero","expected":[[180.0,0.0,0.0,0.0,0.0],80.0],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: daphnia with 2 step lag\", \"actual\": [[10.0, 17.2, 29.584, 50.8845, 84.5904, 132.2425, 184.2037, 206.9118, 153.541], 106.9118], \"expected\": [[10.0, 17.2, 29.584, 50.8845, 84.5904, 132.2425, 184.2037, 206.9118, 153.541], 106.9118], \"passed\": true}, {\"check\": \"regression: no lag\", \"actual\": [[10.0, 14.5, 20.6988, 28.9059, 39.1811, 51.0959, 63.5899], 0.0], \"expected\": [[10.0, 14.5, 20.6988, 28.9059, 39.1811, 51.0959, 63.5899], 0.0], \"passed\": true}, {\"check\": \"regression: long lag oscillation\", \"actual\": [[50.0, 65.0, 84.5, 109.85, 142.805, 172.794, 188.8639, 177.702, 132.0628, 74.3825, 34.723], 88.8639], \"expected\": [[50.0, 65.0, 84.5, 109.85, 142.805, 172.794, 188.8639, 177.702, 132.0628, 74.3825, 34.723], 88.8639], \"passed\": true}, {\"check\": \"regression: start above capacity\", \"actual\": [[150.0, 127.5, 108.375, 99.4341, 96.9358, 97.1004], 27.5], \"expected\": [[150.0, 127.5, 108.375, 99.4341, 96.9358, 97.1004], 50.0], \"passed\": false}, {\"check\": \"regression: one-step lag\", \"actual\": [[20.0, 41.6, 86.528, 168.7642, 283.6643, 336.8272, 167.7447, 30.0325], 136.8272], \"expected\": [[20.0, 41.6, 86.528, 168.7642, 283.6643, 336.8272, 167.7447, 30.0325], 136.8272], \"passed\": true}, {\"check\": \"control: zero steps\", \"actual\": [[30.0], 0.0], \"expected\": [[30.0], 0.0], \"passed\": true}, {\"check\": \"regression: crash to zero\", \"actual\": [[180.0, 0.0, 0.0, 0.0, 0.0], 0.0], \"expected\": [[180.0, 0.0, 0.0, 0.0, 0.0], 80.0], \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":39.522,"exit_code":1,"observations":[{"actual":[[10.0,17.2,29.584,50.8845,84.5904,132.2425,184.2037,206.9118,153.541],196.9118],"check":"regression: daphnia with 2 step lag","expected":[[10.0,17.2,29.584,50.8845,84.5904,132.2425,184.2037,206.9118,153.541],106.9118],"passed":false},{"actual":[[10.0,14.5,20.6988,28.9059,39.1811,51.0959,63.5899],53.5899],"check":"regression: no lag","expected":[[10.0,14.5,20.6988,28.9059,39.1811,51.0959,63.5899],0.0],"passed":false},{"actual":[[50.0,65.0,84.5,109.85,142.805,172.794,188.8639,177.702,132.0628,74.3825,34.723],138.8639],"check":"regression: long lag oscillation","expected":[[50.0,65.0,84.5,109.85,142.805,172.794,188.8639,177.702,132.0628,74.3825,34.723],88.8639],"passed":false},{"actual":[[150.0,127.5,108.375,99.4341,96.9358,97.1004],0.0],"check":"regression: start above capacity","expected":[[150.0,127.5,108.375,99.4341,96.9358,97.1004],50.0],"passed":false},{"actual":[[20.0,41.6,86.528,168.7642,283.6643,336.8272,167.7447,30.0325],316.8272],"check":"regression: one-step lag","expected":[[20.0,41.6,86.528,168.7642,283.6643,336.8272,167.7447,30.0325],136.8272],"passed":false},{"actual":[[30.0],0.0],"check":"control: zero steps","expected":[[30.0],0.0],"passed":true},{"actual":[[180.0,0.0,0.0,0.0,0.0],0.0],"check":"regression: crash to zero","expected":[[180.0,0.0,0.0,0.0,0.0],80.0],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: daphnia with 2 step lag\", \"actual\": [[10.0, 17.2, 29.584, 50.8845, 84.5904, 132.2425, 184.2037, 206.9118, 153.541], 196.9118], \"expected\": [[10.0, 17.2, 29.584, 50.8845, 84.5904, 132.2425, 184.2037, 206.9118, 153.541], 106.9118], \"passed\": false}, {\"check\": \"regression: no lag\", \"actual\": [[10.0, 14.5, 20.6988, 28.9059, 39.1811, 51.0959, 63.5899], 53.5899], \"expected\": [[10.0, 14.5, 20.6988, 28.9059, 39.1811, 51.0959, 63.5899], 0.0], \"passed\": false}, {\"check\": \"regression: long lag oscillation\", \"actual\": [[50.0, 65.0, 84.5, 109.85, 142.805, 172.794, 188.8639, 177.702, 132.0628, 74.3825, 34.723], 138.8639], \"expected\": [[50.0, 65.0, 84.5, 109.85, 142.805, 172.794, 188.8639, 177.702, 132.0628, 74.3825, 34.723], 88.8639], \"passed\": false}, {\"check\": \"regression: start above capacity\", \"actual\": [[150.0, 127.5, 108.375, 99.4341, 96.9358, 97.1004], 0.0], \"expected\": [[150.0, 127.5, 108.375, 99.4341, 96.9358, 97.1004], 50.0], \"passed\": false}, {\"check\": \"regression: one-step lag\", \"actual\": [[20.0, 41.6, 86.528, 168.7642, 283.6643, 336.8272, 167.7447, 30.0325], 316.8272], \"expected\": [[20.0, 41.6, 86.528, 168.7642, 283.6643, 336.8272, 167.7447, 30.0325], 136.8272], \"passed\": false}, {\"check\": \"control: zero steps\", \"actual\": [[30.0], 0.0], \"expected\": [[30.0], 0.0], \"passed\": true}, {\"check\": \"regression: crash to zero\", \"actual\": [[180.0, 0.0, 0.0, 0.0, 0.0], 0.0], \"expected\": [[180.0, 0.0, 0.0, 0.0, 0.0], 80.0], \"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."}}