On Stellar Models with Blanketed Atmospheres as Boundary Conditions
Bibliographic record
Abstract
Abstract The impact on the predicted Teff scale of using the latest MARCS model atmospheres, instead of a fixed atmospheric structure (e.g., the gray T–τ relation) is examined. The former were fitted to stellar interior models at both the photosphere and at τ = 100 to determine the sensitivity of evolutionary tracks and isochrones for [Fe/H] = 0.0 and −2.0 to the chosen fitting point. In the case of solar abundances, the Teff of the giant branch varied by up to 100–150 K, depending on how the outer layers were treated. Much smaller variations were found for metal-poor giants (or main-sequence stars). Interestingly, models for the low solar Z favored by Asplund et al. (Z=0.0125) were unable to reproduce the gap near the turnoff in the C-M diagram of the old open cluster M 67, in contrast to models that assume Z=0.0188.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".