Key comparisons: applying the scientific method to validate uncertainty
Bibliographic record
Abstract
We examine chi-squared statistics that are appropriate for analysing the adequacy of different key comparison reference value candidates in accounting for the observed dispersion of results of a key comparison, about the candidate estimator, and within the stated uncertainty claims. We extend the analysis to cover cases where the uncertainty budgets incorporate low degrees of freedom or have significant correlations. To use these statistics for the usual chi-squared tests of consistency, the required distributions can readily be evaluated by Monte Carlo simulation, including the effects of non-Gaussian uncertainty distributions. Pair-difference chi-squared statistics are a useful method for analysing metrologica, being independent of the choice of estimator of central tendency, and so may expedite the process of analysis, consensus building and publication
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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 | 0.266 | 0.724 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".