Uncertainty analysis of MSL’s CCT-K7.2021 key comparison measurements taking account of correlations
Bibliographic record
Abstract
Abstract The National Research Council of Canada (NRC) recently piloted the second international key comparison of triple point of water cells, CCT-K7.2021, where participants submitted a transfer cell and a report of the temperature difference between their transfer cell and their respective national reference. By comparing the differences between each transfer cell, NRC could calculate the differences between each participant’s national reference. While there was good agreement among all the participants, there was a large variation among the submitted uncertainties. This was possibly due to the complexity of the uncertainty analysis, which typically has many correlated components between the transfer cell and the national reference that partially cancel when the difference is taken. The Measurement Standards Laboratory of New Zealand (MSL) carried out its analysis using a detailed measurement model that included all known correlations and applied an algorithmic approach fully compliant with the Guide to the Expression of Uncertainty in Measurement. As a consequence, MSL submitted an uncertainty value less than half the uncertainty values submitted by almost all of the other participants. This paper describes the measurement model implemented by MSL and demonstrates how accounting correctly for correlations leads to a significantly lower uncertainty. The paper also shows how applying the same method to the previously published NRC analysis results in a reduction in the NRC uncertainty by a factor of about 2.5, making it smaller than the MSL uncertainty.
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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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".