MODELS FOR METAL-POOR STARS WITH ENHANCED ABUNDANCES OF C, N, O, Ne, Na, Mg, Si, S, Ca, AND Ti, IN TURN, AT CONSTANT HELIUM AND IRON ABUNDANCES
Bibliographic record
Abstract
Recent work has shown that most globular clusters have at least two chemically distinct components, as well as cluster-to-cluster differences in the mean [O/Fe], [Mg/Fe], and [Si/Fe] ratios at similar [Fe/H] values. In order to investigate the implications of variations in the abundances of these and other metals for H-R diagrams and predicted ages, grids of evolutionary sequences have been computed for scaled solar and enhanced α-element metal abundances, and for mixtures in which the assumed [ m /Fe] value for each of the metals C, N, O, Ne, Na, Mg, Si, S, Ca, and Ti has been increased, in turn, by 0.4 dex at constant [Fe/H]. These tracks, together with isochrones for ages from ≈5 to 14 Gyr, have been computed for −3.0 ⩽ [Fe/H] ⩽−0.6, with helium abundances Y = 0.25, 0.29, and 0.33 at each [Fe/H] value, using upgraded versions of the Victoria stellar structure program and the Regina interpolation code, respectively. Turnoff luminosity versus age relations from isochrones are found to depend almost entirely on the importance of the CNO cycle, and thereby mainly on the abundance of oxygen. Since C, N, and O, as well as Ne and S, do not contribute significantly to the opacities at low temperatures and densities, variations in their abundances do not impact the predicted T eff scale of red giants. The latter is a strong function of the abundances of only Mg and Si (and Fe, possibly to a lesser extent) because they are so abundant and because they are strong sources of opacity at low temperatures. For these reasons, Mg and Si also have important effects on the temperatures of main-sequence stars. Due to their low abundances, Na, Ca, and Ti are of little consequence for stellar models. The effects of varying the adopted solar metals mixture and the helium abundance at a fixed [Fe/H] are also briefly discussed.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".