Détermination des abondances des éléments chimiques d'étoiles A et F naines membres de deux amas ouverts. Contraintes sur les modèles évolutifs incluant les processus de transport.
Bibliographic record
Abstract
We observe abundance anomalies at the surface of many field and cluster A stars. Theseabundance anomalies are generated by microscopic diffusion acting within the stable envelopes and may be modulated by the competition between many other mixing processes such as convection, rotational mixing and mass loss. The typical patterns encountered are underabundances of Ca and/or Sc as well as overabundances of iron peek elements and rare earths. Non magnetic A stars with such patterns are classified as Am. By observing the surface of A and F stars from open clusters in particular, we can infer additional stellar parameters such as age and initial chemical composition which are invaluable for the comparison with models. Therefore, by determining the surface composition of A and F stars from many clusters of different ages we can follow the evolution of chemical composition and constrain the processes considered in evolutionary models. In this thesis, the surface abundances of 22 elements are presented for 21 A/F stars of the Pleiades open cluster (100 Myr) and for 22 A/F stars of the Coma Berenices open cluster (450 Myr) which were observed with the three spectrographs AURELIE, ELODIE and SOPHIE at the Observatoire de Haute-Provence (OHP). The synthetic spectra method, which is based on ATLAS9-12 atmospheric models, is used in order to determine the abundances. These observations are then compared with evolutionary models from the Montreal group. The results show that there are hydrodynamical processes acting within the radiative zone of these stars and hindering the effects of microscopic diffusion.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".