Bibliographic record
Abstract
A specific sub-set of comparison data is presented for two NIST water-triple-point cells that provide traceability to the results of the 2023 Consultative Committee for Thermometry CCT-K7.2021 CIPM Key Comparison of Water-Triple-Point Cells. NIST had initially been an official participant in the CCT-K7.2021 but decided to withdraw from the comparison after the breakage of two NIST transfer cells. Despite this status as a withdrawn former participant, enough NIST data was taken prior to the comparison to provide a direct linkage to the results of the K7.1 using the measurements on a NIST transfer cell performed by the Pilot Laboratory, the National Research Council of Canada (NRC). The NIST data was obtained from routine scale maintenance and research activities, and hence did not conform to the official protocol of the CCT-K7.2021, but these data are of good quality and sufficient to establish an overall transfer uncertainty of less than 0.1 mK. The linkage does not convey an official status suitable for establishing Calibration Measurement Capabilities (CMCs) under the Mutual Recognition Arrangement (MRA). However, the linkage is sufficient to provide evidence of traceability to the CCT-K7.2021 and supports NIST Quality System requirements for thermometric standards under the ISO 17025(2017).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".