The primordial and evolutionary abundance variations in globular-cluster stars: a problem with two unknowns
Bibliographic record
Abstract
We demonstrate that among the potential sources of the primordial abundance variations of the proton-capture elements in globular-cluster stars proposed so far, such as the hot-bottom burning in massive asymptotic giant branch stars and H burning in the convective cores of supermassive and fast-rotating massive main-sequence (MS) stars, only the supermassive MS stars with M > 104 M⊙ can explain all the observed abundance correlations without any fine-tuning of model parameters. We use our assumed chemical composition for the pristine gas in M13 (NGC 6205) and its mixtures with 50 and 90 per cent of the material partially processed in H burning in the 6 × 104 M⊙ MS model star as the initial compositions for the normal, intermediate, and extreme populations of low-mass stars in this globular cluster, as suggested by its O–Na anticorrelation. We evolve these stars from the zero-age MS to the red giant branch (RGB) tip with the thermohaline and parametric prescriptions for the RGB extra mixing. We find that the 3He-driven thermohaline convection cannot explain the evolutionary decline of [C/Fe] in M13 RGB stars, which, on the other hand, is well reproduced with the universal values for the mixing depth and rate calibrated using the observed decrease of [C/Fe] with MV in the globular cluster NGC5466 that does not have the primordial abundance variations.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".