Why every CAI value needs an authority record
Two CAI values for the same sequence were both mathematically correct—and still misleading when shown without their references.
The apparent discrepancy
The same output sequence received different CAI values in two evaluation paths. The formulas were not broken. Each calculation used a different codon reference.
One path evaluated against an expression-oriented reference. Another evaluated against the working table used during generation. Without a label, both numbers appeared under the same field name and invited a comparison they could not support.
From a number to a record
The corrective action was additive: preserve the numerical result and attach its authority. A useful authority record answers at least four questions:
- Which reference was used?
- What role did that reference play?
- Was it distinct from the generation reference?
- Was a fallback used?
An initial two-state model—preferred reference or fallback—was still incomplete. It could not represent the valid case where a caller intentionally selects one reference for both generation and evaluation. The final relationship therefore needed three states: distinct, intentionally the same, and fallback to the generation reference.
Why labels are part of the result
Metric provenance is not administrative decoration. A CAI value without its reference cannot be reproduced or interpreted reliably, and two unlabeled CAI values may measure different relationships while looking identical in a table.
The same principle applies to model-derived scores and validation summaries. A result should identify the authority that produced it and the relationship between that authority and the candidate-generation path. Otherwise, later readers are forced to reconstruct meaning from implementation details.