Opacity Sampling in Radiative Acceleration Calculations
Bibliographic record
Abstract
The accuracy requirements on atomic data for the calculation of stellar evolution with atomic diffusion are determined. In particular, the density of frequency grids needed for precise radiative acceleration ( g rad ) calculations via the sampling method are presented. In order to minimize the number of frequency points needed for precise g rad calculations, frequency grids that are more refined in the regions of the spectrum where the radiative flux is large are suggested. The following number of frequency points are needed for opacity table calculations to be used in stellar evolutionary codes including diffusion: 50,000 points for 4.20 ≤ log T ≤ 4.5, 30,000 points for 4.5 < log T ≤ 4.8, 10,000 points for 4.8 < log T ≤ 5.5, and 4000 points for log T > 5.5. These opacity tables would render possible the study atomic diffusion in the exterior regions of certain chemically peculiar stars such as Ap or HgMn stars. In the sampling method, correction factors can be applied after the basic integrations over sampled spectra to include such effects as ion velocity averaging, redistribution of momentum among ions, and electron recoil during photoionization; these corrections are evaluated and illustrated for a few typical stellar models. Silicon is used as an example to show that the corrections are important mainly for T < 50,000 K. These corrections are used in stellar evolution calculations based on OPAL monochromatic opacity tables.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".