{"abstract":"Recruitment tracks the lagged population instead of current breeders.","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].","evaluation_group":"w2-ecopop-delayed-logistic","failed_approach":"Starting from the lagged density discards the current state.","family":"w2-ecopop-delayed-logistic-growth-base","id":"FA-65741","implementations":{"attempt":{"sha256":"30bb6771dc97dd7330fd288d5cd66c6a5d8d93135db6684a8bc0c29245ada78d","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, past + r * now * (1 - past / k)))\n    over = max(0.0, max(hist) - 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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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":"c15748843abf505bbaca8e16106881cd786c432303aa144c83db75fd200e96b7","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 * past * (1 - past / k)))\n    over = max(0.0, max(hist) - 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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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"},"fixed":{"sha256":"3ae662246968217c12068c39e69306df69947a259935090e8ef25b0dcc2af779","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) - 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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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  ('control: no lag', (10, 0.5, 100, 0, 6), [[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-growth-base","generated_at":"2026-09-29T14:47:36.661945+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.","repair":"Restore the growth base rule: `now + r * now * (1 - past / k)`.","root_cause":"Per-capita growth multiplies the lagged density.","sha256":"804d2002acb69f18f8544e6c3b9208dd93d074ad448f8aa8cb8decd225673141","title":"Discrete delayed logistic (Hutchinson) with lag: growth base · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":39.498,"exit_code":1,"observations":[{"actual":[[10.0,17.2,22.384,26.1165,34.4996,43.8057,52.0087,61.7523,71.5667],0.0],"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],0.0],"check":"control: no lag","expected":[[10.0,14.5,20.6988,28.9059,39.1811,51.0959,63.5899],0.0],"passed":true},{"actual":[[50.0,65.0,69.5,70.85,71.255,79.9635,84.1333,85.5649,86.0124,90.3038,92.7303],0.0],"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":[[150.0,127.5,130.875,116.7028,120.0654,110.6865],50.0],"check":"regression: start above 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