Modeling the near-ultraviolet band of GK stars. III. dependence on abundance pattern
Bibliographic record
Abstract
We extend the grid of NLTE models presented in Paper II to explore variations in abundance pattern in two ways: 1) The adoption of the Asplund et al. (2009) (GASS10) abundances, 2) For stars of metallicity, [M/H], of -0.5, the adoption of a non-solar enhancement of alpha-elements by +0.3 dex. Moreover, our grid of synthetic spectral energy distributions (SEDs) is interpolated to a finer numerical resolution in both T_eff (Delta T_eff = 25 K) and log g (Delta log g = 0.25). We compare the values of T_eff and log g inferred from fitting LTE and Non-LTE SEDs to observed SEDs throughout the entire visible band, and in an ad hoc "blue" band. We compare our spectrophotometrically derived T_eff values to a variety of T_eff calibrations, including more empirical ones, drawn from the literature. For stars of solar metallicity, we find that the adoption of the GASS10 abundances lowers the inferred T_eff value by 25 - 50 K for late-type giants, and NLTE models computed with the GASS10 abundances give T_eff results that are marginally in better agreement with other T_eff calibrations. For stars of [M/H]=-0.5 there is marginal evidence that adoption of alpha-enhancement further lowers the derived T_eff value by 50 K. Stellar parameters inferred from fitting NLTE models to SEDs are more dependent than LTE models on the wavelength region being fitted, and we find that the effect depends on how heavily line blanketed the fitting region is, whether the fitting region is to the blue of the Wien peak of the star's SED, or both.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".