Refining the cold atom pressure standard
Bibliographic record
Abstract
Abstract In our previous work (Shen et al 2020 Metrologia 57 025015; Booth et al 2019 New J. Phys. 21 102001), we have reported the first primary quantum pressure standard based on the loss rate of cold rubidium atoms from a magnetic trap. We have shown that this standard is self-calibrating. That is, the single parameter required to quantify the pressure or particle flux impinging on the sensor atoms, σ t o t v , can be determined experimentally. In this paper, we refine our procedure to extract the trap loss rate coefficient by expressing the measured trap loss rate of the rubidium atoms as a convolution of the universal loss rate expression from Shen et al (2020 Metrologia 57 025015) and Booth et al (2019 New J. Phys. 21 102001) with the energy distribution of the rubidium atoms in the trap. We report improved σ t o t v values for 87Rb–X (X = He, Ar, Xe, H2, N2, and CO2) collision pairs. All are systematically higher than our previously reported values, although the differences are less than 2%. The calibration factor of an ionization gauge for nitrogen obtained using the cold atom standard, i g = 0.950 (19), agrees with the value obtained by NIST, i g = 0.940 (26) calibrated against their orifice flow standard.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".