MODELING THE NEAR-ULTRAVIOLET BAND OF GK STARS. I. LOCAL THERMODYNAMIC EQUILIBRIUM MODELS
Bibliographic record
Abstract
We present a grid of LTE atmospheric models and synthetic spectra that covers the spectral class range from mid-G to mid-K, and luminosity classes from V to III, that is dense in T eff sampling (Δ T eff = 62.5 K), for stars of solar metallicity and moderately metal-poor scaled solar abundance ( and −0.5). All models have been computed with two choices of atomic line list: (1) the "big" line lists of Kurucz that best reproduce the broadband solar blue and near-UV f λ level, and (2) the "small" lists of Kurucz & Peytremann that provide the best fit to the high-resolution solar blue and near-UV spectrum. We compare our model spectral energy distributions to a sample of stars carefully selected from the large catalog of uniformly re-calibrated spectrophotometry of Burnashev with the goal of determining how the quality of fit varies with stellar parameters, especially in the historically troublesome blue and near-UV bands. We confirm that our models computed with the "big" line list recover the derived T eff values of the PHOENIX NextGen grid, but find that the models computed with the "small" line list provide greater internal self-consistency among different spectral bands, and closer agreement with the empirical T eff scale of Ramirez & Melendez, but not to the interferometrically derived T eff values of Baines et al. We find no evidence that the near-UV band discrepancy between models and observations for Arcturus (α Boo) reported in two works by Short & Hauschildt is pervasive, and that Arcturus may be peculiar in this regard.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".