Bibliographic record
Abstract
We present LTE and non-LTE (NLTE) atmospheric models of a star with solar parameters and study the effect of treating many thousands of iron-group lines out of LTE on the computed atmospheric structure, the overall absolute flux distribution, and the moderately high resolution spectrum in the visible and near-UV bands. Our NLTE modeling includes the first two or three ionization stages of 20 chemical elements, up to and including much of the Fe group, and includes about 20,000 Fe I and II lines. We investigate separately the effects of treating the light metals and the Fe-group elements in NLTE. Our main conclusions are that (1) NLTE line-blanketed models with direct multilevel NLTE for many actual transitions give results qualitatively similar to those of the more approximate treatment of L. S. Anderson for both the Fe statistical equilibrium and the atmospheric T kin structure; (2) models with many Fe lines in NLTE have a T kin structure that agrees more closely with LTE semiempirical models based on center-to-limb variation and a wide variety of spectra lines, whereas LTE models agree more with semiempirical models based only on an LTE calculation of the Fe I excitation equilibrium; and (3) the NLTE effects of Fe-group elements on the model structure and F λ distribution are much more important than the NLTE effects of all the light metals combined and serve to substantially increase the violet and near-UV F λ level as a result of NLTE Fe overionization. These results suggest that there may still be important UV opacity missing from the models. However, the choice of the species and multiplet-dependent van der Waals broadening enhancement also plays a significant role in determining whether LTE or NLTE models provide a close fit to the near-UV flux level. We also find that the rms deviation of the shape of the rectified high-resolution synthetic spectrum from that of the observed spectrum is not significantly affected by the inclusion of NLTE effects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".