Population II stars and the Spite plateau; Stellar evolution models with mass loss
Bibliographic record
Abstract
We aim to determine the constraints that observed chemical abundances put on the potential role of mass loss in metal poor dwarfs. Self-consistent stellar evolutionary models that include all the effects of atomic diffusion and radiative accelerations for 28 chemical species were computed for stellar masses between 0.6 and 0.8 Msun. Models with an initial metallicity of Z_0=0.00017 and mass loss rates from 10e-15 Msun to 10e-12 Msun were calculated. They were then compared to previous models with mass loss, as well as to models with turbulent mixing. For models with an initial metallicity of Fe/H=-2.31, mass loss rates of about 10e-12 Msun lead to surface abundance profiles that are very similar to those obtained in models with turbulence. Both models have about the same level of agreement with observations of galactic-halo lithium abundances, as well as lithium and other elemental abundances from metal poor globular clusters such as NGC 6397. In this cluster, models with mass loss agree slightly better with subgiant observations of Li abundance than those with turbulence. Lower red giant branch stars instead favor the models with turbulence. Larger differences between models with mass loss and those with turbulence appear in the interior concentrations of metals. The relatively high mass loss rates required to reproduce plateau-like lithium abundances appear unlikely when compared to the solar mass-loss rate. However the presence of a chromosphere on these stars justifies further investigation of the mass-loss rates.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".