Bibliographic record
Abstract
The redefinition of the kelvin has increased focus on thermometry techniques that use the newly fixed value of the Boltzmann constant to realize thermodynamic temperature. One such technique that has advanced considerably in recent years is refractive-index gas thermometry. Generalized as refractive-index gas metrology (RIGM), this also includes a range of applications outside of temperature realizations, such as pressure standards and measurements of the physical properties of gases. Here, the current data situation in the field is reviewed, encompassing the latest developments and remaining challenges, in order to suggest possible approaches for reducing RIGM uncertainties and improving RIGM applications. New analyses of existing experimental literature data are presented for the second density virial coefficient Bρ of helium, neon, argon, and nitrogen; the third density virial coefficient Cρ of nitrogen; and the third dielectric virial coefficient Cε of helium, neon, and argon. A need is identified for more accurate reference-quality datasets to be measured or calculated in several areas, with robust uncertainty budgets, to support future RIGM advancements. The most urgent of these are the bulk modulus of copper; thermodynamic accuracy of the International Temperature Scale of 1990; molar optical refractivity AR of neon, argon, and nitrogen; diamagnetic susceptibility χ0 of neon and argon; second density virial coefficient Bρ of argon; third dielectric virial coefficient Cε of helium, neon, and argon; and third optical refractivity virial coefficient CR of helium and neon.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".