FA-12396 / Laboratory measurement reporting / Open access
Aliquot blank is subtracted after dilution correction · case 01
Aliquot blank is subtracted after dilution correction.
ROOT CAUSE
Blank and sample values expressed on the aliquot basis are combined on different bases.
VERIFIED REPAIR
Subtract the aliquot blank before scaling to the original sample.
Unsuccessful approach: Clamping negative aliquot differences to zero conceals signed blank-corrected results.
Case contract
Return signed (signal - aliquot_blank) * dilution as a Fraction string. Signals are rational strings and dilution is positive. Negative results remain reportable in this model.
Why this case matters
A deterministic synthetic laboratory reporting model isolates a software metadata contract; it is not a clinical procedure.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(signal, blank, dilution):
return str(Fraction(signal)*dilution-Fraction(blank))
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('blank scales with aliquot', solve(str(5*N), str(N), 3), str(12*N))
check('negative blank correction retained', solve(str(N), str(2*N), 4), str(-4*N))
check('blank equality', solve(str(N), str(N), 5), '0')
check('undiluted sample', solve(str(3*N), str(N), 1), str(2*N))
check('zero blank', solve(str(N), '0', 3), str(3*N))
check('fractional blank', solve('1', '1/2', 3), '3/2')
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 |
|---|---|---|---|
| blank scales with aliquot | 14 | 12 | Failed |
| negative blank correction retained | 2 | -4 | Failed |
| blank equality | 4 | 0 | Failed |
| undiluted sample | 2 | 2 | Passed |
| zero blank | 3 | 3 | Passed |
| fractional blank | 5/2 | 3/2 | Failed |
SHA-256 / 1aadf6598537f2899bb76dffffcf9c21c05458dc3dec3d416905c37b0d2f9612
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(signal, blank, dilution):
return str(max(Fraction(0), Fraction(signal)-Fraction(blank))*dilution)
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('blank scales with aliquot', solve(str(5*N), str(N), 3), str(12*N))
check('negative blank correction retained', solve(str(N), str(2*N), 4), str(-4*N))
check('blank equality', solve(str(N), str(N), 5), '0')
check('undiluted sample', solve(str(3*N), str(N), 1), str(2*N))
check('zero blank', solve(str(N), '0', 3), str(3*N))
check('fractional blank', solve('1', '1/2', 3), '3/2')
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 |
|---|---|---|---|
| blank scales with aliquot | 12 | 12 | Passed |
| negative blank correction retained | 0 | -4 | Failed |
| blank equality | 0 | 0 | Passed |
| undiluted sample | 2 | 2 | Passed |
| zero blank | 3 | 3 | Passed |
| fractional blank | 3/2 | 3/2 | Passed |
SHA-256 / 9d5fea203290069dcb48dbac8d13a6b1d6eb384e66469327e53a7062280641b0
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(signal, blank, dilution):
return str((Fraction(signal)-Fraction(blank))*dilution)
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('blank scales with aliquot', solve(str(5*N), str(N), 3), str(12*N))
check('negative blank correction retained', solve(str(N), str(2*N), 4), str(-4*N))
check('blank equality', solve(str(N), str(N), 5), '0')
check('undiluted sample', solve(str(3*N), str(N), 1), str(2*N))
check('zero blank', solve(str(N), '0', 3), str(3*N))
check('fractional blank', solve('1', '1/2', 3), '3/2')
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 |
|---|---|---|---|
| blank scales with aliquot | 12 | 12 | Passed |
| negative blank correction retained | -4 | -4 | Passed |
| blank equality | 0 | 0 | Passed |
| undiluted sample | 2 | 2 | Passed |
| zero blank | 3 | 3 | Passed |
| fractional blank | 3/2 | 3/2 | Passed |
SHA-256 / f426cb741a4191a436feb4351e1193e1c107c86e9284983bf9e4e2e946e67a24
Verification & scope
Synthetic integer/rational fixtures only; no instrument validation or clinical interpretation. 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 2026-09-29T14:38:56.568518+00:00.
Case digest / c109f32eea6828e711bfbdb29f3a6bfbd3e1bef4ea6db4548ab3dfe7dfcc9c2d