Survey of subrange inconsistency of long-stem standard platinum resistance thermometers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The subrange inconsistency (SRI) of a large ensemble of long-stem standard platinum resistance thermometers (SPRTs), representative of worldwide production, has been investigated for all pairs of ITS-90 overlapping subranges between 83.8058 K and 933.473 K. The results are reported in terms of various statistical parameters to facilitate their comparison with the results of different authors, although a statistical test, applied to one specific pair of subranges, supported a Gaussian distribution of the results and justified the subsequent use of the mean and standard deviation as statistical parameters. Depending on the pair of overlapping subranges, the mean SRI as calculated varied from −1.23 mK to +0.21 mK and the SRI standard deviation varied from 0.04 mK to 0.62 mK. These numbers generally increased with the upper temperature limit of the pair of subranges, especially where the lower subrange requires a point which is not included in the upper subrange. The contribution to SRI from the fixed-point uncertainty propagation (PoU) was evaluated. The results showed that, although the effect of PoU on SRI largely cancels out for points common to both subranges, PoU still amounts to 59% to 130% of the differences between overlapping pairs of subranges. This means that the differences as calculated are probably a substantial overestimate of the true SRI. It is suggested that this effect is taken into account in making recommendations for typical uncertainties due to non-uniqueness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it