FA-65571 / Ecological population dynamics / Open access

Schaefer surplus-production harvest reference points: status boundary · case 01

Fishing exactly at E_msy is labelled overfished.

Verified by executionVariant 1 · 7 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The overfished comparison includes equality.

VERIFIED REPAIR

Restore the status boundary rule: `if effort > emsy:`.

Unsuccessful approach: A one-unit tolerance mislabels slightly excessive effort as sustainable.

Case contract

MSY = rK/4 at B=K/2 and E_msy = r/(2q); equilibrium biomass under effort E is K(1-qE/r) when qE<r else 0; yield = qEB; status 'overfished' when E>E_msy, 'at-msy' when equal, else 'sustainable'; return [MSY, Bmsy, Emsy, B, yield, status] rounded; None for invalid inputs.

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(r, k, effort, q):
    if r <= 0 or k <= 0 or q <= 0 or effort < 0:
        return None
    msy = r * k / 4
    bmsy = k / 2
    emsy = r / (2 * q)
    b = k * (1 - q * effort / r) if q * effort < r else 0.0
    y = q * effort * b
    if effort >= emsy:
        status = 'overfished'
    elif effort == emsy:
        status = 'at-msy'
    else:
        status = 'sustainable'
    return [round(msy, 4), round(bmsy, 4), round(emsy, 6), round(b, 4), round(y, 4), status]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('control: tuna fishery moderate effort',
   (0.4, 10000, 100, 0.001),
   [1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']),
  ('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: overfished', (0.3, 5000, 250, 0.001), [375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('control: no fishing', (0.6, 800, 0, 0.002), [120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']),
  ('control: high catchability', (0.8, 1500, 50, 0.01), [300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished'])],
 [('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('control: no fishing', (0.6, 800, 0, 0.002), [120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']),
  ('control: high catchability', (0.8, 1500, 50, 0.01), [300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished']),
  ('control: invalid q', (0.5, 2000, 100, 0.0), None),
  ('control: light fishing', (1.2, 400, 20, 0.005), [120.0, 200.0, 120.0, 366.6667, 36.6667, 'sustainable'])],
 [('control: tuna fishery moderate effort',
   (0.4, 10000, 100, 0.001),
   [1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']),
  ('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: overfished', (0.3, 5000, 250, 0.001), [375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished']),
  ('control: invalid q', (0.5, 2000, 100, 0.0), None),
  ('control: light fishing', (1.2, 400, 20, 0.005), [120.0, 200.0, 120.0, 366.6667, 36.6667, 'sustainable'])],
 [('control: tuna fishery moderate effort',
   (0.4, 10000, 100, 0.001),
   [1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']),
  ('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: overfished', (0.3, 5000, 250, 0.001), [375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('control: no fishing', (0.6, 800, 0, 0.002), [120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']),
  ('control: high catchability', (0.8, 1500, 50, 0.01), [300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished'])],
 [('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('control: no fishing', (0.6, 800, 0, 0.002), [120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']),
  ('control: high catchability', (0.8, 1500, 50, 0.01), [300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished']),
  ('control: invalid q', (0.5, 2000, 100, 0.0), None),
  ('control: light fishing', (1.2, 400, 20, 0.005), [120.0, 200.0, 120.0, 366.6667, 36.6667, 'sustainable'])]]
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
control: tuna fishery moderate effort	`[1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']`	`[1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']`	Passed
regression: effort at msy	`[250.0, 1000.0, 250.0, 1000.0, 250.0, 'overfished']`	`[250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']`	Failed
control: overfished	`[375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']`	`[375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']`	Passed
control: collapse effort	`[150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']`	`[150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']`	Passed
control: no fishing	`[120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']`	`[120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']`	Passed
control: high catchability	`[300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']`	`[300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']`	Passed
regression: slightly over msy	`[250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished']`	`[250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished']`	Passed

SHA-256 / 1ce00ca6ed99b163ff0efeeca1c33aff5b105aa367a4ef2d3b4a0964929ec81b

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(r, k, effort, q):
    if r <= 0 or k <= 0 or q <= 0 or effort < 0:
        return None
    msy = r * k / 4
    bmsy = k / 2
    emsy = r / (2 * q)
    b = k * (1 - q * effort / r) if q * effort < r else 0.0
    y = q * effort * b
    if effort > emsy + 1:
        status = 'overfished'
    elif effort == emsy:
        status = 'at-msy'
    else:
        status = 'sustainable'
    return [round(msy, 4), round(bmsy, 4), round(emsy, 6), round(b, 4), round(y, 4), status]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('control: tuna fishery moderate effort',
   (0.4, 10000, 100, 0.001),
   [1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']),
  ('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: overfished', (0.3, 5000, 250, 0.001), [375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('control: no fishing', (0.6, 800, 0, 0.002), [120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']),
  ('control: high catchability', (0.8, 1500, 50, 0.01), [300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished'])],
 [('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('control: no fishing', (0.6, 800, 0, 0.002), [120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']),
  ('control: high catchability', (0.8, 1500, 50, 0.01), [300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished']),
  ('control: invalid q', (0.5, 2000, 100, 0.0), None),
  ('control: light fishing', (1.2, 400, 20, 0.005), [120.0, 200.0, 120.0, 366.6667, 36.6667, 'sustainable'])],
 [('control: tuna fishery moderate effort',
   (0.4, 10000, 100, 0.001),
   [1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']),
  ('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: overfished', (0.3, 5000, 250, 0.001), [375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished']),
  ('control: invalid q', (0.5, 2000, 100, 0.0), None),
  ('control: light fishing', (1.2, 400, 20, 0.005), [120.0, 200.0, 120.0, 366.6667, 36.6667, 'sustainable'])],
 [('control: tuna fishery moderate effort',
   (0.4, 10000, 100, 0.001),
   [1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']),
  ('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: overfished', (0.3, 5000, 250, 0.001), [375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('control: no fishing', (0.6, 800, 0, 0.002), [120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']),
  ('control: high catchability', (0.8, 1500, 50, 0.01), [300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished'])],
 [('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('control: no fishing', (0.6, 800, 0, 0.002), [120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']),
  ('control: high catchability', (0.8, 1500, 50, 0.01), [300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished']),
  ('control: invalid q', (0.5, 2000, 100, 0.0), None),
  ('control: light fishing', (1.2, 400, 20, 0.005), [120.0, 200.0, 120.0, 366.6667, 36.6667, 'sustainable'])]]
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
control: tuna fishery moderate effort	`[1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']`	`[1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']`	Passed
regression: effort at msy	`[250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']`	`[250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']`	Passed
control: overfished	`[375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']`	`[375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']`	Passed
control: collapse effort	`[150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']`	`[150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']`	Passed
control: no fishing	`[120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']`	`[120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']`	Passed
control: high catchability	`[300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']`	`[300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']`	Passed
regression: slightly over msy	`[250.0, 1000.0, 250.0, 998.4, 249.9994, 'sustainable']`	`[250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished']`	Failed

SHA-256 / 0203ef2c68a83ac963c8f93334da7a13fa06985a6829fd0b581dd01d6f2a5125

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(r, k, effort, q):
    if r <= 0 or k <= 0 or q <= 0 or effort < 0:
        return None
    msy = r * k / 4
    bmsy = k / 2
    emsy = r / (2 * q)
    b = k * (1 - q * effort / r) if q * effort < r else 0.0
    y = q * effort * b
    if effort > emsy:
        status = 'overfished'
    elif effort == emsy:
        status = 'at-msy'
    else:
        status = 'sustainable'
    return [round(msy, 4), round(bmsy, 4), round(emsy, 6), round(b, 4), round(y, 4), status]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('control: tuna fishery moderate effort',
   (0.4, 10000, 100, 0.001),
   [1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']),
  ('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: overfished', (0.3, 5000, 250, 0.001), [375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('control: no fishing', (0.6, 800, 0, 0.002), [120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']),
  ('control: high catchability', (0.8, 1500, 50, 0.01), [300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished'])],
 [('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('control: no fishing', (0.6, 800, 0, 0.002), [120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']),
  ('control: high catchability', (0.8, 1500, 50, 0.01), [300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished']),
  ('control: invalid q', (0.5, 2000, 100, 0.0), None),
  ('control: light fishing', (1.2, 400, 20, 0.005), [120.0, 200.0, 120.0, 366.6667, 36.6667, 'sustainable'])],
 [('control: tuna fishery moderate effort',
   (0.4, 10000, 100, 0.001),
   [1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']),
  ('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: overfished', (0.3, 5000, 250, 0.001), [375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished']),
  ('control: invalid q', (0.5, 2000, 100, 0.0), None),
  ('control: light fishing', (1.2, 400, 20, 0.005), [120.0, 200.0, 120.0, 366.6667, 36.6667, 'sustainable'])],
 [('control: tuna fishery moderate effort',
   (0.4, 10000, 100, 0.001),
   [1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']),
  ('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: overfished', (0.3, 5000, 250, 0.001), [375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('control: no fishing', (0.6, 800, 0, 0.002), [120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']),
  ('control: high catchability', (0.8, 1500, 50, 0.01), [300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished'])],
 [('regression: effort at msy', (0.5, 2000, 250, 0.001), [250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']),
  ('control: collapse effort', (0.2, 3000, 300, 0.001), [150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']),
  ('control: no fishing', (0.6, 800, 0, 0.002), [120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']),
  ('control: high catchability', (0.8, 1500, 50, 0.01), [300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']),
  ('regression: slightly over msy',
   (0.5, 2000, 250.4, 0.001),
   [250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished']),
  ('control: invalid q', (0.5, 2000, 100, 0.0), None),
  ('control: light fishing', (1.2, 400, 20, 0.005), [120.0, 200.0, 120.0, 366.6667, 36.6667, 'sustainable'])]]
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
control: tuna fishery moderate effort	`[1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']`	`[1000.0, 5000.0, 200.0, 7500.0, 750.0, 'sustainable']`	Passed
regression: effort at msy	`[250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']`	`[250.0, 1000.0, 250.0, 1000.0, 250.0, 'at-msy']`	Passed
control: overfished	`[375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']`	`[375.0, 2500.0, 150.0, 833.3333, 208.3333, 'overfished']`	Passed
control: collapse effort	`[150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']`	`[150.0, 1500.0, 100.0, 0.0, 0.0, 'overfished']`	Passed
control: no fishing	`[120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']`	`[120.0, 400.0, 150.0, 800.0, 0.0, 'sustainable']`	Passed
control: high catchability	`[300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']`	`[300.0, 750.0, 40.0, 562.5, 281.25, 'overfished']`	Passed
regression: slightly over msy	`[250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished']`	`[250.0, 1000.0, 250.0, 998.4, 249.9994, 'overfished']`	Passed

SHA-256 / 2001816aeefc7bb9726d567c5e4b9aa19e768769c801468c3f032918e7d9d809

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 2026-09-29T14:47:35.298683+00:00.

Case digest / 993b94deb036cdd12639724ba85ae6550484ae817c5ebfb92975e5d6bbb00427