Bibliographic record
Abstract
We propose new criteria to help select a subset of peer results for determining a weighted-mean Key Comparison Reference Value (KCRV) for describing Key Comparisons supporting the CIPM Mutual Recognition Arrangement (MRA). Often the results are all well behaved: their scatter is in the range expected from the Gaussian probability distributions associated with their stated standard uncertainties. In this case, the KCRV would usually be the inverse-variance weighted mean of all eligible results, the maximum likelihood estimate of independent Gaussian probability distributions. If most—but not all—of the results are well behaved, the comparison may be better described in terms of a KCRV computed with 'outliers' excluded from the weighted mean. An outlier's effects are quantified in terms of the logarithmic slope and logarithmic curvature of its associated probability density function in the vicinity of the consensus value. The outer tails of the probability distributions are generally not known with confidence, so two new criteria are suggested to help justify the identification of outliers: one based on uncertainty in the logarithmic slope, and the other based on uncertainty about whether the logarithmic curvature is negative definite.
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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 | 0.026 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".