Assessing Binary Measurement Systems: A Cost-Effective Alternative to Complete Verification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Suppose we plan to assess a binary measurement system when the misclassification probabilities vary from part to part. We consider the estimation of the average error probabilities of such a system when a gold standard (error-free) system is available to verify the status of any part. We examine plans where we first measure a sample of n parts r times each with the binary measurement system. Then we study the impact on the precision and robustness of the estimates if we use the gold-standard system to verify the true status of none, some, or all of the sampled parts. We show that a partial verification plan has comparable performance to full verification in terms of the precision and robustness of the estimates while requiring as few as 10% of parts to be verified. When the gold-standard system is expensive or time consuming, eliminating the need to verify all parts dramatically reduces the cost of the assessment study.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it