FA-65776 / Ecological population dynamics / Open access
Monthly population with seasonal breeding window: inclusive window end · case 01
The last breeding month produces no births.
ROOT CAUSE
The breeding window excludes its end month.
VERIFIED REPAIR
Restore the inclusive window end rule: `breeding = breed_start <= month <= breed_end`.
Unsuccessful approach: Excluding the start month drops the first breeding month.
Case contract
Calendar month of step t is ((start_month-1+t) mod 12)+1; the breeding window is inclusive and may wrap across the new year (start > end); each month survival is applied first, then in breeding months births fecundity*N*max(0,1-N/cap); return monthly N rounded 4.
Why this case matters
Population projections set harvest quotas, conservation status and pest-control timing; a wrong update order, boundary or rate conversion silently changes management advice.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(n0, monthly_survival, fecundity, breed_start, breed_end, start_month, months, cap):
n = float(n0)
traj = []
for t in range(months):
month = (start_month - 1 + t) % 12 + 1
if breed_start <= breed_end:
breeding = breed_start <= month < breed_end
else:
breeding = month >= breed_start or month <= breed_end
n *= monthly_survival
if breeding:
n += fecundity * n * max(0.0, 1 - n / cap)
traj.append(round(n, 4))
return traj
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('control: southern winter breeding wraps',
(50, 0.95, 0.3, 11, 2, 10, 6, 300),
[47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636]),
('control: december start', (80, 0.98, 0.25, 12, 1, 12, 3, 500), [94.9267, 111.9581, 109.7189]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('control: above cap no births', (500, 0.99, 0.3, 1, 12, 1, 3, 400), [495.0, 490.05, 485.1495]),
('control: no breeding months reached', (40, 0.95, 0.3, 8, 9, 1, 4, 200), [38.0, 36.1, 34.295, 32.5802]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377])],
[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('control: above cap no births', (500, 0.99, 0.3, 1, 12, 1, 3, 400), [495.0, 490.05, 485.1495]),
('control: no breeding months reached', (40, 0.95, 0.3, 8, 9, 1, 4, 200), [38.0, 36.1, 34.295, 32.5802]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377]),
('regression: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407]),
('control: start inside wrap', (70, 0.93, 0.35, 10, 3, 1, 4, 250), [81.9518, 94.7582, 108.0965, 100.5297])],
[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('control: southern winter breeding wraps',
(50, 0.95, 0.3, 11, 2, 10, 6, 300),
[47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636]),
('control: december start', (80, 0.98, 0.25, 12, 1, 12, 3, 500), [94.9267, 111.9581, 109.7189]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377]),
('regression: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407]),
('control: start inside wrap', (70, 0.93, 0.35, 10, 3, 1, 4, 250), [81.9518, 94.7582, 108.0965, 100.5297])],
[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('control: southern winter breeding wraps',
(50, 0.95, 0.3, 11, 2, 10, 6, 300),
[47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636]),
('control: december start', (80, 0.98, 0.25, 12, 1, 12, 3, 500), [94.9267, 111.9581, 109.7189]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('control: above cap no births', (500, 0.99, 0.3, 1, 12, 1, 3, 400), [495.0, 490.05, 485.1495]),
('control: no breeding months reached', (40, 0.95, 0.3, 8, 9, 1, 4, 200), [38.0, 36.1, 34.295, 32.5802]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377])],
[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('control: above cap no births', (500, 0.99, 0.3, 1, 12, 1, 3, 400), [495.0, 490.05, 485.1495]),
('control: no breeding months reached', (40, 0.95, 0.3, 8, 9, 1, 4, 200), [38.0, 36.1, 34.295, 32.5802]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377]),
('regression: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407]),
('control: start inside wrap', (70, 0.93, 0.35, 10, 3, 1, 4, 250), [81.9518, 94.7582, 108.0965, 100.5297])]]
for label, args, expected in fixtures[N - 1]:
check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression: deer spring births | [97.0, 94.09, 119.4445, 115.8612, 112.3853, 109.0138] | [97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888] | Failed |
| control: southern winter breeding wraps | [47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636] | [47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636] | Passed |
| control: december start | [94.9267, 111.9581, 109.7189] | [94.9267, 111.9581, 109.7189] | Passed |
| regression: single month window end | [54.0, 48.6, 43.74] | [54.0, 66.9951, 60.2956] | Failed |
| control: above cap no births | [495.0, 490.05, 485.1495] | [495.0, 490.05, 485.1495] | Passed |
| control: no breeding months reached | [38.0, 36.1, 34.295, 32.5802] | [38.0, 36.1, 34.295, 32.5802] | Passed |
| regression: full year | [28.8, 27.648, 30.4415, 33.3606, 32.0262, 30.7451, 29.5153, 28.3347, 27.2013, 26.1133, 25.0687, 24.066] | [28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377] | Failed |
SHA-256 / abc00de1713514ff2b882f06aa655d555522882b625c37cbda0f3f5e9bed325a
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(n0, monthly_survival, fecundity, breed_start, breed_end, start_month, months, cap):
n = float(n0)
traj = []
for t in range(months):
month = (start_month - 1 + t) % 12 + 1
if breed_start <= breed_end:
breeding = breed_start < month <= breed_end
else:
breeding = month >= breed_start or month <= breed_end
n *= monthly_survival
if breeding:
n += fecundity * n * max(0.0, 1 - n / cap)
traj.append(round(n, 4))
return traj
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('control: southern winter breeding wraps',
(50, 0.95, 0.3, 11, 2, 10, 6, 300),
[47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636]),
('control: december start', (80, 0.98, 0.25, 12, 1, 12, 3, 500), [94.9267, 111.9581, 109.7189]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('control: above cap no births', (500, 0.99, 0.3, 1, 12, 1, 3, 400), [495.0, 490.05, 485.1495]),
('control: no breeding months reached', (40, 0.95, 0.3, 8, 9, 1, 4, 200), [38.0, 36.1, 34.295, 32.5802]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377])],
[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('control: above cap no births', (500, 0.99, 0.3, 1, 12, 1, 3, 400), [495.0, 490.05, 485.1495]),
('control: no breeding months reached', (40, 0.95, 0.3, 8, 9, 1, 4, 200), [38.0, 36.1, 34.295, 32.5802]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377]),
('regression: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407]),
('control: start inside wrap', (70, 0.93, 0.35, 10, 3, 1, 4, 250), [81.9518, 94.7582, 108.0965, 100.5297])],
[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('control: southern winter breeding wraps',
(50, 0.95, 0.3, 11, 2, 10, 6, 300),
[47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636]),
('control: december start', (80, 0.98, 0.25, 12, 1, 12, 3, 500), [94.9267, 111.9581, 109.7189]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377]),
('regression: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407]),
('control: start inside wrap', (70, 0.93, 0.35, 10, 3, 1, 4, 250), [81.9518, 94.7582, 108.0965, 100.5297])],
[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('control: southern winter breeding wraps',
(50, 0.95, 0.3, 11, 2, 10, 6, 300),
[47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636]),
('control: december start', (80, 0.98, 0.25, 12, 1, 12, 3, 500), [94.9267, 111.9581, 109.7189]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('control: above cap no births', (500, 0.99, 0.3, 1, 12, 1, 3, 400), [495.0, 490.05, 485.1495]),
('control: no breeding months reached', (40, 0.95, 0.3, 8, 9, 1, 4, 200), [38.0, 36.1, 34.295, 32.5802]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377])],
[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('control: above cap no births', (500, 0.99, 0.3, 1, 12, 1, 3, 400), [495.0, 490.05, 485.1495]),
('control: no breeding months reached', (40, 0.95, 0.3, 8, 9, 1, 4, 200), [38.0, 36.1, 34.295, 32.5802]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377]),
('regression: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407]),
('control: start inside wrap', (70, 0.93, 0.35, 10, 3, 1, 4, 250), [81.9518, 94.7582, 108.0965, 100.5297])]]
for label, args, expected in fixtures[N - 1]:
check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression: deer spring births | [97.0, 94.09, 91.2673, 116.1036, 112.6205, 109.2418] | [97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888] | Failed |
| control: southern winter breeding wraps | [47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636] | [47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636] | Passed |
| control: december start | [94.9267, 111.9581, 109.7189] | [94.9267, 111.9581, 109.7189] | Passed |
| regression: single month window end | [54.0, 48.6, 43.74] | [54.0, 66.9951, 60.2956] | Failed |
| control: above cap no births | [495.0, 490.05, 485.1495] | [495.0, 490.05, 485.1495] | Passed |
| control: no breeding months reached | [38.0, 36.1, 34.295, 32.5802] | [38.0, 36.1, 34.295, 32.5802] | Passed |
| regression: full year | [28.8, 27.648, 26.5421, 29.278, 32.1482, 30.8623, 29.6278, 28.4427, 27.305, 26.2128, 25.1643, 24.1577] | [28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377] | Failed |
SHA-256 / 0ec84b127853791b35af3b673741fa4ea68545f1de953cf6215eb2f8f5563232
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(n0, monthly_survival, fecundity, breed_start, breed_end, start_month, months, cap):
n = float(n0)
traj = []
for t in range(months):
month = (start_month - 1 + t) % 12 + 1
if breed_start <= breed_end:
breeding = breed_start <= month <= breed_end
else:
breeding = month >= breed_start or month <= breed_end
n *= monthly_survival
if breeding:
n += fecundity * n * max(0.0, 1 - n / cap)
traj.append(round(n, 4))
return traj
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('control: southern winter breeding wraps',
(50, 0.95, 0.3, 11, 2, 10, 6, 300),
[47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636]),
('control: december start', (80, 0.98, 0.25, 12, 1, 12, 3, 500), [94.9267, 111.9581, 109.7189]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('control: above cap no births', (500, 0.99, 0.3, 1, 12, 1, 3, 400), [495.0, 490.05, 485.1495]),
('control: no breeding months reached', (40, 0.95, 0.3, 8, 9, 1, 4, 200), [38.0, 36.1, 34.295, 32.5802]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377])],
[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('control: above cap no births', (500, 0.99, 0.3, 1, 12, 1, 3, 400), [495.0, 490.05, 485.1495]),
('control: no breeding months reached', (40, 0.95, 0.3, 8, 9, 1, 4, 200), [38.0, 36.1, 34.295, 32.5802]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377]),
('regression: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407]),
('control: start inside wrap', (70, 0.93, 0.35, 10, 3, 1, 4, 250), [81.9518, 94.7582, 108.0965, 100.5297])],
[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('control: southern winter breeding wraps',
(50, 0.95, 0.3, 11, 2, 10, 6, 300),
[47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636]),
('control: december start', (80, 0.98, 0.25, 12, 1, 12, 3, 500), [94.9267, 111.9581, 109.7189]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377]),
('regression: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407]),
('control: start inside wrap', (70, 0.93, 0.35, 10, 3, 1, 4, 250), [81.9518, 94.7582, 108.0965, 100.5297])],
[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('control: southern winter breeding wraps',
(50, 0.95, 0.3, 11, 2, 10, 6, 300),
[47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636]),
('control: december start', (80, 0.98, 0.25, 12, 1, 12, 3, 500), [94.9267, 111.9581, 109.7189]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('control: above cap no births', (500, 0.99, 0.3, 1, 12, 1, 3, 400), [495.0, 490.05, 485.1495]),
('control: no breeding months reached', (40, 0.95, 0.3, 8, 9, 1, 4, 200), [38.0, 36.1, 34.295, 32.5802]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377])],
[('regression: deer spring births',
(100, 0.97, 0.4, 5, 6, 3, 6, 400),
[97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888]),
('regression: single month window end', (60, 0.9, 0.5, 4, 4, 3, 3, 200), [54.0, 66.9951, 60.2956]),
('control: above cap no births', (500, 0.99, 0.3, 1, 12, 1, 3, 400), [495.0, 490.05, 485.1495]),
('control: no breeding months reached', (40, 0.95, 0.3, 8, 9, 1, 4, 200), [38.0, 36.1, 34.295, 32.5802]),
('regression: full year',
(30, 0.96, 0.2, 3, 5, 1, 12, 100),
[28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377]),
('regression: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407]),
('control: start inside wrap', (70, 0.93, 0.35, 10, 3, 1, 4, 250), [81.9518, 94.7582, 108.0965, 100.5297])]]
for label, args, expected in fixtures[N - 1]:
check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression: deer spring births | [97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888] | [97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888] | Passed |
| control: southern winter breeding wraps | [47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636] | [47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636] | Passed |
| control: december start | [94.9267, 111.9581, 109.7189] | [94.9267, 111.9581, 109.7189] | Passed |
| regression: single month window end | [54.0, 66.9951, 60.2956] | [54.0, 66.9951, 60.2956] | Passed |
| control: above cap no births | [495.0, 490.05, 485.1495] | [495.0, 490.05, 485.1495] | Passed |
| control: no breeding months reached | [38.0, 36.1, 34.295, 32.5802] | [38.0, 36.1, 34.295, 32.5802] | Passed |
| regression: full year | [28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377] | [28.8, 27.648, 30.4415, 33.3606, 36.38, 34.9248, 33.5278, 32.1867, 30.8993, 29.6633, 28.4768, 27.3377] | Passed |
SHA-256 / 13ed8411cf80c9bbfd33f007305e5924c75d44b90b93d3183db21bd6e5c25378
Verification & scope
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.
Observations recorded using Python 3.12.14 at 2026-09-29T14:47:37.221464+00:00.
Case digest / c6bfe8a8f4162e420a768b6e04fe02141a749b9e8f32c4ade8c036b79626978a