FA-12406 / Laboratory measurement reporting / Open access
Calibration lookup borrows coefficients from another matrix · case 01
Calibration lookup borrows coefficients from another matrix.
ROOT CAUSE
Calibration records are indexed by lot alone despite matrix-specific fits.
THE FAILURE
Calibration records are indexed by lot alone despite matrix-specific fits.
Unsuccessful approach: Filtering by matrix alone fixes one collision but can borrow a different lot.
Case contract
Given unique [lot,matrix,slope] records, return the slope for an exact pair or None. No fallback between lots or matrices.
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
N = 1
observations = []
def solve(records, lot, matrix):
return next((s for l,m,s in records if l == lot), None)
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
r = [('A','water',N),('A','soil',N+2),('B','soil',N+4)]
check('same lot different matrix', solve(r,'A','soil'), N+2)
check('same matrix different lot', solve(r,'B','soil'), N+4)
check('missing pair', solve(r,'B','water'), None)
check('first exact pair', solve(r,'A','water'), N)
check('unknown lot', solve(r,'C','soil'), None)
check('empty calibration table', solve([], 'A','water'), None)
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 |
|---|---|---|---|
| same lot different matrix | 1 | 3 | Failed |
| same matrix different lot | 5 | 5 | Passed |
| missing pair | 5 | None | Failed |
| first exact pair | 1 | 1 | Passed |
| unknown lot | None | None | Passed |
| empty calibration table | None | None | Passed |
SHA-256 / b9e6bbdd413dd00f220495823d9cffd1f991f711b2956134c96d12adf6a65266
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(records, lot, matrix):
return next((s for l,m,s in records if m == matrix), None)
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
r = [('A','water',N),('A','soil',N+2),('B','soil',N+4)]
check('same lot different matrix', solve(r,'A','soil'), N+2)
check('same matrix different lot', solve(r,'B','soil'), N+4)
check('missing pair', solve(r,'B','water'), None)
check('first exact pair', solve(r,'A','water'), N)
check('unknown lot', solve(r,'C','soil'), None)
check('empty calibration table', solve([], 'A','water'), None)
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 |
|---|---|---|---|
| same lot different matrix | 3 | 3 | Passed |
| same matrix different lot | 3 | 5 | Failed |
| missing pair | 1 | None | Failed |
| first exact pair | 1 | 1 | Passed |
| unknown lot | 3 | None | Failed |
| empty calibration table | None | None | Passed |
SHA-256 / 7ac7cbb9a186faaad4ecfe78d36a425ca3b21e1cbe4deff89d23b9ee2b024e5d
HELD IN THE MEMBER ARCHIVE
The verified repair and its recorded checks are member-only.
This mechanism has 6 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.
Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.
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Sign in to the archive ↗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 2026-09-29T14:38:56.645386+00:00.
Case digest / 4258c153fc4c1af1557a6e3b55db4815785886c8a7a6ce55f174519b446696d4