FA-65766 / Ecological population dynamics / Open access
Monthly population with seasonal breeding window: month wrap · case 01
December is treated as month 0 and every month is shifted.
ROOT CAUSE
The calendar month is computed without the 1-based offset.
VERIFIED REPAIR
Restore the month wrap rule: `month = (start_month - 1 + t) % 12 + 1`.
Unsuccessful approach: Dropping the +1 after the modulus maps January to 0.
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 + t) % 12
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]),
('regression: 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]),
('regression: 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: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407])],
[('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]),
('regression: 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]),
('regression: 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: 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]),
('regression: 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]),
('regression: 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]),
('regression: 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: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407])],
[('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]),
('regression: 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 |
| regression: 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 |
| regression: 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: late-autumn window through december | [78.4, 96.3397, 94.4129, 92.5247] | [78.4, 96.3397, 117.3885, 115.0407] | Failed |
SHA-256 / cf8f14b1b411d166eac7e34ff7c55c103f6417ad5974e1d2fab964eb9828200f
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 + t - 1) % 12
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]),
('regression: 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]),
('regression: 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: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407])],
[('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]),
('regression: 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]),
('regression: 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: 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]),
('regression: 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]),
('regression: 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]),
('regression: 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: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407])],
[('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]),
('regression: 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, 144.9853, 140.6357] | [97.0, 94.09, 119.4445, 148.7818, 144.3184, 139.9888] | Failed |
| regression: southern winter breeding wraps | [47.5, 45.125, 53.8916, 63.935, 75.2706, 87.846] | [47.5, 56.6262, 67.0395, 78.7377, 91.6459, 87.0636] | Failed |
| regression: december start | [78.4, 93.0884, 109.8722] | [94.9267, 111.9581, 109.7189] | Failed |
| regression: single month window end | [54.0, 48.6, 60.827] | [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: late-autumn window through december | [78.4, 76.832, 94.4823, 92.5927] | [78.4, 96.3397, 117.3885, 115.0407] | Failed |
SHA-256 / 84a61a2bc296088a2d0117ceaf16e9a4e6f85de148335cbba091464d4f68e28e
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]),
('regression: 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]),
('regression: 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: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407])],
[('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]),
('regression: 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]),
('regression: 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: 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]),
('regression: 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]),
('regression: 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]),
('regression: 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: late-autumn window through december',
(80, 0.98, 0.3, 11, 12, 10, 4, 500),
[78.4, 96.3397, 117.3885, 115.0407])],
[('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]),
('regression: 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 |
| regression: 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 |
| regression: 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: late-autumn window through december | [78.4, 96.3397, 117.3885, 115.0407] | [78.4, 96.3397, 117.3885, 115.0407] | Passed |
SHA-256 / 19f02dec1ac2450843dcbe9a875a4b41f2426168787d6be17631d0ea5cb537d8
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 2026-09-29T14:47:37.135137+00:00.
Case digest / df5cf44d6c7d6f54c3605e77b0a42090dcdcb0e0e594d18e6a05cd680bbd6be0