Assessment of a Binary Measurement System in Current Use
Bibliographic record
Abstract
Binary measurement systems that classify parts as pass or fail are widely used in industry, especially for systematic inspection in high-volume processes. In this context, we are likely to have available a large number of previously measured passed and failed parts. To support production and quality improvement, it is important to assess the misclassification rates, e.g., the probability of failing a conforming part or passing a nonconforming part. We may also want to estimate the unknown conforming rate. Here we focus on the assessment of a binary measurement system when no gold-standard measurement system is available. The standard assessment plan is to repeatedly measure a sample of parts and use a latent class model. We demonstrate the substantial benefit of supplementing the standard plan with the available data from the previously measured parts. We propose new sampling plans and compare them with the standard plan with respect to the precision of the estimators of the misclassification rates. We also give recommendations for planning an assessment study when we can sample from a population of previously measured parts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".