The MiMeS survey of magnetism in massive stars: CNO surface abundances of Galactic O stars
Bibliographic record
Abstract
Context. The evolution of massive stars is still partly unconstrained. Mass, metallicity, mass loss, and rotation are the main drivers of stellar evolution. Binarity and the magnetic field may also significantly affect the fate of massive stars. Aims. Our goal is to investigate the evolution of single O stars in the Galaxy. Methods. For that, we used a sample of 74 objects comprising all luminosity classes and spectral types from O4 to O9.7. We relied on optical spectroscopy obtained in the context of the MiMeS survey of massive stars. We performed spectral modelling with the code CMFGEN. We determined the surface properties of the sample stars, with special emphasis on abundances of carbon, nitrogen, and oxygen. Results. Most of our sample stars have initial masses in the range of 20 to 50 M⊙. We show that nitrogen is more enriched and carbon and oxygen are more depleted in supergiants than in dwarfs, with giants showing intermediate degrees of mixing. CNO abundances are observed in the range of values predicted by nucleosynthesis through the CNO cycle. More massive stars, within a given luminosity class, appear to be more chemically enriched than lower mass stars. We compare our results with predictions of three types of evolutionary models and show that for two sets of models, 80% of our sample can be explained by stellar evolution including rotation. The effect of magnetism on surface abundances is unconstrained. Conclusions. Our study indicates that in the 20−50 M⊙ mass range, the surface chemical abundances of most single O stars in the Galaxy are fairly well accounted for by stellar evolution of rotating stars.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".