The Abundance Evolution of Oxygen, Sodium and Magnesium in Extremely Metal-Poor Intermediate Mass Stars: Implications for the Self-Polution Scenario in Globular Clusters
Bibliographic record
Abstract
We present full stellar evolution and parametric models of the surface abundance evolution of O16, Ne22, Na23 and the magnesium isotopes in an extremely metal-poor intermediate mass star M_ZAMS=5M_sun, Z=0.0001. O16 and Ne22 are injected into the envelope by the third dredge-up following thermal pulses on the asymptotic giant branch. These species and the initially present Mg24 are depleted by hot bottom burning (HBB) during the interpulse phase. As a result, Na23, Mg25 and Mg26 are enhanced. If the HBB temperatures are sufficiently high for this process to deplete oxygen efficiently, Na23 is first produced and then depleted during the interpulse phase. Although the simultaneous depletion of O16 and enhancement of Na23 is possible, the required fine tuning of the dredge-up and HBB casts some doubt on the robustness of this process as the origin of the O-Na anti-correlation observed in globular cluster stars. However, a very robust prediction of our models are low Mg24/Mg25 and Mg24/Mg26 ratios whenever significant O16 depletion can be achieved. This seems to be in stark contrast with recent observations of the magnesium isotopic ratios in the globular cluster NGC6752.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".