Prevalence Determines Precision:Silent Contamination in Detector-Defined Datasets
Quick summary
arXiv:2609.11449v1 Announce Type: cross Abstract: Many ML datasets are constructed by running a detector, heuristic, or model over candidate pools; accepted items become labels. Dataset precision is then governed by true-positive prevalence in each pool via Bayes, not solely by detector quality. Using one instrument and period, we hold a detector-defined event dataset plus an independent official index labeling every detected item as real or phantom. One detector, three pools yield phantom rates 81.7%, 9.0%, and 0.0%. Transferring precision from the two high-rate pools to the low-rate pool pre
Key takeaways
- arXiv:2609.11449v1 Announce Type: cross Abstract: Many ML datasets are constructed by running a detector, heuristic, or model over candidate pools; accepted items become labels.
- Dataset precision is then governed by true-positive prevalence in each pool via Bayes, not solely by detector quality.
- Using one instrument and period, we hold a detector-defined event dataset plus an independent official index labeling every detected item as real or phantom.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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