FA-97271 / Recipe scaling and nutrition / Open access

Macronutrient target splitter: protein floor · case 01

A 120 kg athlete on 1500 kcal gets 56 g protein.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The 1.6 g/kg protein floor is not applied.

VERIFIED REPAIR

Apply ceiling(1.6*weight) as a floor.

Unsuccessful approach: Flooring the per-kg target lets protein fall a gram short.

Case contract

split = [protein%, carb%, fat%] must sum to 100 else "error: split". protein_g = max(half-up kcal*p/400, ceiling(1.6*weight_kg)). Remaining kcal = kcal - 4*protein_g is divided between carb and fat in the ratio c:f; fat_g = half-up(rem*f/(c+f)/9); carb_g = half-up((rem - 9*fat_g)/4). Return [protein_g, carb_g, fat_g].

Why this case matters

Meal-plan generators turn calorie targets into gram targets with a protein floor by body weight.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
import math
from fractions import Fraction
N = 1
observations = []
def solve(kcal, weight_kg, split):
    p, c, f = split
    if p + c + f != 100:
        return 'error: split'
    def r(x):
        return math.floor(x + Fraction(1, 2))
    protein_g = max(r(Fraction(kcal * p, 400)), 0)
    rem = kcal - 4 * protein_g
    fat_g = r(Fraction(rem * f, c + f) / 9)
    carb_g = r(Fraction(rem - 9 * fat_g, 4))
    return [protein_g, carb_g, fat_g]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['balanced', [2000, 70, [30, 40, 30]], [150, 199, 67]],
  ['protein floor binds', [1500, 120, [15, 55, 30]], [192, 118, 29]],
  ['bad split', [2000, 70, [30, 40, 31]], 'error: split'],
  ['regression: protein floor', [2500, 91, [15, 55, 30]], [146, 310, 75]],
  ['repair check: protein floor', [1500, 118, [30, 40, 30]], [189, 107, 35]],
  ['generated control 1', [3000, 120, [20, 50, 25]], 'error: split'],
  ['generated control 2', [1500, 118, [30, 40, 29]], 'error: split'],
  ['generated control 3', [2000, 50, [40, 30, 30]], [200, 149, 67]]],
 [['protein floor binds', [1500, 120, [15, 55, 30]], [192, 118, 29]],
  ['bad split', [2000, 70, [30, 40, 31]], 'error: split'],
  ['split under 100', [2000, 70, [30, 40, 20]], 'error: split'],
  ['regression: protein floor', [1500, 118, [25, 45, 30]], [189, 112, 33]],
  ['repair check: protein floor', [1800, 118, [25, 45, 30]], [189, 158, 46]],
  ['generated control 1', [2200, 91, [40, 30, 30]], [220, 166, 73]],
  ['generated control 2', [2200, 118, [40, 30, 30]], [220, 166, 73]],
  ['generated control 3', [2500, 72, [20, 50, 30]], [125, 313, 83]]],
 [['bad split', [2000, 70, [30, 40, 31]], 'error: split'],
  ['split under 100', [2000, 70, [30, 40, 20]], 'error: split'],
  ['fractional floor', [1200, 83, [20, 50, 30]], [133, 104, 28]],
  ['regression: protein floor', [1500, 100, [25, 45, 30]], [160, 130, 38]],
  ['repair check: protein floor', [2000, 118, [10, 60, 30]], [189, 208, 46]],
  ['generated control 1', [2200, 50, [10, 60, 30]], [80, 313, 70]],
  ['generated control 2', [1500, 50, [35, 35, 30]], [131, 132, 50]],
  ['generated control 3', [1500, 60, [30, 40, 29]], 'error: split']],
 [['split under 100', [2000, 70, [30, 40, 20]], 'error: split'],
  ['fractional floor', [1200, 83, [20, 50, 30]], [133, 104, 28]],
  ['balanced', [2000, 70, [30, 40, 30]], [150, 199, 67]],
  ['regression: protein floor', [2000, 60, [15, 55, 30]], [96, 262, 63]],
  ['repair check: protein floor', [1800, 118, [40, 30, 30]], [189, 131, 58]],
  ['generated control 1', [2500, 100, [35, 35, 30]], [219, 219, 83]],
  ['generated control 2', [3000, 83, [30, 40, 29]], 'error: split'],
  ['generated control 3', [1500, 60, [30, 40, 29]], 'error: split']],
 [['fractional floor', [1200, 83, [20, 50, 30]], [133, 104, 28]],
  ['balanced', [2000, 70, [30, 40, 30]], [150, 199, 67]],
  ['protein floor binds', [1500, 120, [15, 55, 30]], [192, 118, 29]],
  ['regression: protein floor', [2500, 120, [15, 55, 30]], [192, 280, 68]],
  ['repair check: protein floor', [3000, 91, [15, 55, 30]], [146, 390, 95]],
  ['generated control 1', [3000, 91, [35, 35, 30]], [263, 262, 100]],
  ['generated control 2', [2000, 72, [25, 45, 30]], [125, 224, 67]],
  ['generated control 3', [3000, 50, [15, 55, 30]], [113, 412, 100]]]]
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
balanced	`[150, 199, 67]`	`[150, 199, 67]`	Passed
protein floor binds	`[56, 207, 50]`	`[192, 118, 29]`	Failed
bad split	`error: split`	`error: split`	Passed
regression: protein floor	`[94, 344, 83]`	`[146, 310, 75]`	Failed
repair check: protein floor	`[113, 150, 50]`	`[189, 107, 35]`	Failed
generated control 1	`error: split`	`error: split`	Passed
generated control 2	`error: split`	`error: split`	Passed
generated control 3	`[200, 149, 67]`	`[200, 149, 67]`	Passed

SHA-256 / 8b6cbc5fac9463172b55385ec217da3c125926fd4d771227a81edaca9907d6b5

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json
import math
from fractions import Fraction
N = 1
observations = []
def solve(kcal, weight_kg, split):
    p, c, f = split
    if p + c + f != 100:
        return 'error: split'
    def r(x):
        return math.floor(x + Fraction(1, 2))
    protein_g = max(r(Fraction(kcal * p, 400)), math.floor(Fraction(16, 10) * weight_kg))
    rem = kcal - 4 * protein_g
    fat_g = r(Fraction(rem * f, c + f) / 9)
    carb_g = r(Fraction(rem - 9 * fat_g, 4))
    return [protein_g, carb_g, fat_g]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['balanced', [2000, 70, [30, 40, 30]], [150, 199, 67]],
  ['protein floor binds', [1500, 120, [15, 55, 30]], [192, 118, 29]],
  ['bad split', [2000, 70, [30, 40, 31]], 'error: split'],
  ['regression: protein floor', [2500, 91, [15, 55, 30]], [146, 310, 75]],
  ['repair check: protein floor', [1500, 118, [30, 40, 30]], [189, 107, 35]],
  ['generated control 1', [3000, 120, [20, 50, 25]], 'error: split'],
  ['generated control 2', [1500, 118, [30, 40, 29]], 'error: split'],
  ['generated control 3', [2000, 50, [40, 30, 30]], [200, 149, 67]]],
 [['protein floor binds', [1500, 120, [15, 55, 30]], [192, 118, 29]],
  ['bad split', [2000, 70, [30, 40, 31]], 'error: split'],
  ['split under 100', [2000, 70, [30, 40, 20]], 'error: split'],
  ['regression: protein floor', [1500, 118, [25, 45, 30]], [189, 112, 33]],
  ['repair check: protein floor', [1800, 118, [25, 45, 30]], [189, 158, 46]],
  ['generated control 1', [2200, 91, [40, 30, 30]], [220, 166, 73]],
  ['generated control 2', [2200, 118, [40, 30, 30]], [220, 166, 73]],
  ['generated control 3', [2500, 72, [20, 50, 30]], [125, 313, 83]]],
 [['bad split', [2000, 70, [30, 40, 31]], 'error: split'],
  ['split under 100', [2000, 70, [30, 40, 20]], 'error: split'],
  ['fractional floor', [1200, 83, [20, 50, 30]], [133, 104, 28]],
  ['regression: protein floor', [1500, 100, [25, 45, 30]], [160, 130, 38]],
  ['repair check: protein floor', [2000, 118, [10, 60, 30]], [189, 208, 46]],
  ['generated control 1', [2200, 50, [10, 60, 30]], [80, 313, 70]],
  ['generated control 2', [1500, 50, [35, 35, 30]], [131, 132, 50]],
  ['generated control 3', [1500, 60, [30, 40, 29]], 'error: split']],
 [['split under 100', [2000, 70, [30, 40, 20]], 'error: split'],
  ['fractional floor', [1200, 83, [20, 50, 30]], [133, 104, 28]],
  ['balanced', [2000, 70, [30, 40, 30]], [150, 199, 67]],
  ['regression: protein floor', [2000, 60, [15, 55, 30]], [96, 262, 63]],
  ['repair check: protein floor', [1800, 118, [40, 30, 30]], [189, 131, 58]],
  ['generated control 1', [2500, 100, [35, 35, 30]], [219, 219, 83]],
  ['generated control 2', [3000, 83, [30, 40, 29]], 'error: split'],
  ['generated control 3', [1500, 60, [30, 40, 29]], 'error: split']],
 [['fractional floor', [1200, 83, [20, 50, 30]], [133, 104, 28]],
  ['balanced', [2000, 70, [30, 40, 30]], [150, 199, 67]],
  ['protein floor binds', [1500, 120, [15, 55, 30]], [192, 118, 29]],
  ['regression: protein floor', [2500, 120, [15, 55, 30]], [192, 280, 68]],
  ['repair check: protein floor', [3000, 91, [15, 55, 30]], [146, 390, 95]],
  ['generated control 1', [3000, 91, [35, 35, 30]], [263, 262, 100]],
  ['generated control 2', [2000, 72, [25, 45, 30]], [125, 224, 67]],
  ['generated control 3', [3000, 50, [15, 55, 30]], [113, 412, 100]]]]
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
balanced	`[150, 199, 67]`	`[150, 199, 67]`	Passed
protein floor binds	`[192, 118, 29]`	`[192, 118, 29]`	Passed
bad split	`error: split`	`error: split`	Passed
regression: protein floor	`[145, 311, 75]`	`[146, 310, 75]`	Failed
repair check: protein floor	`[188, 106, 36]`	`[189, 107, 35]`	Failed
generated control 1	`error: split`	`error: split`	Passed
generated control 2	`error: split`	`error: split`	Passed
generated control 3	`[200, 149, 67]`	`[200, 149, 67]`	Passed

SHA-256 / 239b21bbc5273d50e7224e1984db0a376defc1f01e9be708b31151cbfdafc844

3 / The verified repair

Exit 0

"""Failure Map reference implementation. Python standard library only."""
import json
import math
from fractions import Fraction
N = 1
observations = []
def solve(kcal, weight_kg, split):
    p, c, f = split
    if p + c + f != 100:
        return 'error: split'
    def r(x):
        return math.floor(x + Fraction(1, 2))
    protein_g = max(r(Fraction(kcal * p, 400)), math.ceil(Fraction(16, 10) * weight_kg))
    rem = kcal - 4 * protein_g
    fat_g = r(Fraction(rem * f, c + f) / 9)
    carb_g = r(Fraction(rem - 9 * fat_g, 4))
    return [protein_g, carb_g, fat_g]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['balanced', [2000, 70, [30, 40, 30]], [150, 199, 67]],
  ['protein floor binds', [1500, 120, [15, 55, 30]], [192, 118, 29]],
  ['bad split', [2000, 70, [30, 40, 31]], 'error: split'],
  ['regression: protein floor', [2500, 91, [15, 55, 30]], [146, 310, 75]],
  ['repair check: protein floor', [1500, 118, [30, 40, 30]], [189, 107, 35]],
  ['generated control 1', [3000, 120, [20, 50, 25]], 'error: split'],
  ['generated control 2', [1500, 118, [30, 40, 29]], 'error: split'],
  ['generated control 3', [2000, 50, [40, 30, 30]], [200, 149, 67]]],
 [['protein floor binds', [1500, 120, [15, 55, 30]], [192, 118, 29]],
  ['bad split', [2000, 70, [30, 40, 31]], 'error: split'],
  ['split under 100', [2000, 70, [30, 40, 20]], 'error: split'],
  ['regression: protein floor', [1500, 118, [25, 45, 30]], [189, 112, 33]],
  ['repair check: protein floor', [1800, 118, [25, 45, 30]], [189, 158, 46]],
  ['generated control 1', [2200, 91, [40, 30, 30]], [220, 166, 73]],
  ['generated control 2', [2200, 118, [40, 30, 30]], [220, 166, 73]],
  ['generated control 3', [2500, 72, [20, 50, 30]], [125, 313, 83]]],
 [['bad split', [2000, 70, [30, 40, 31]], 'error: split'],
  ['split under 100', [2000, 70, [30, 40, 20]], 'error: split'],
  ['fractional floor', [1200, 83, [20, 50, 30]], [133, 104, 28]],
  ['regression: protein floor', [1500, 100, [25, 45, 30]], [160, 130, 38]],
  ['repair check: protein floor', [2000, 118, [10, 60, 30]], [189, 208, 46]],
  ['generated control 1', [2200, 50, [10, 60, 30]], [80, 313, 70]],
  ['generated control 2', [1500, 50, [35, 35, 30]], [131, 132, 50]],
  ['generated control 3', [1500, 60, [30, 40, 29]], 'error: split']],
 [['split under 100', [2000, 70, [30, 40, 20]], 'error: split'],
  ['fractional floor', [1200, 83, [20, 50, 30]], [133, 104, 28]],
  ['balanced', [2000, 70, [30, 40, 30]], [150, 199, 67]],
  ['regression: protein floor', [2000, 60, [15, 55, 30]], [96, 262, 63]],
  ['repair check: protein floor', [1800, 118, [40, 30, 30]], [189, 131, 58]],
  ['generated control 1', [2500, 100, [35, 35, 30]], [219, 219, 83]],
  ['generated control 2', [3000, 83, [30, 40, 29]], 'error: split'],
  ['generated control 3', [1500, 60, [30, 40, 29]], 'error: split']],
 [['fractional floor', [1200, 83, [20, 50, 30]], [133, 104, 28]],
  ['balanced', [2000, 70, [30, 40, 30]], [150, 199, 67]],
  ['protein floor binds', [1500, 120, [15, 55, 30]], [192, 118, 29]],
  ['regression: protein floor', [2500, 120, [15, 55, 30]], [192, 280, 68]],
  ['repair check: protein floor', [3000, 91, [15, 55, 30]], [146, 390, 95]],
  ['generated control 1', [3000, 91, [35, 35, 30]], [263, 262, 100]],
  ['generated control 2', [2000, 72, [25, 45, 30]], [125, 224, 67]],
  ['generated control 3', [3000, 50, [15, 55, 30]], [113, 412, 100]]]]
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
balanced	`[150, 199, 67]`	`[150, 199, 67]`	Passed
protein floor binds	`[192, 118, 29]`	`[192, 118, 29]`	Passed
bad split	`error: split`	`error: split`	Passed
regression: protein floor	`[146, 310, 75]`	`[146, 310, 75]`	Passed
repair check: protein floor	`[189, 107, 35]`	`[189, 107, 35]`	Passed
generated control 1	`error: split`	`error: split`	Passed
generated control 2	`error: split`	`error: split`	Passed
generated control 3	`[200, 149, 67]`	`[200, 149, 67]`	Passed

SHA-256 / 30facebc75308d0251a67e7a4ff9d0b156de6aa7dede51f5d02ed25aef432037

Verification & scope

A deterministic toy contract stated in full here; it is a bounded teaching model, not an authoritative reference or standards implementation. 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 2026-09-29T14:52:30.533801+00:00.

Case digest / 7be8c0797ba6b08588ecc60bfeb5bb3cf56d88fca75ffa808feba89551198c13