Constraints on Stellar Hydrodynamics from Abundance Anomalies of LiBeB and Metals
Bibliographic record
Abstract
The availability of large atomic data bases has made it possible to calculate stellar evolution models taking into detailed account the atomic diffusion of all important contributors to opacity. The radiative accelerations and the opacity are continuously calculated during evolution taking the abundance changes of 28 species into account. This leads to the first self-consistent stellar evolution models for A and F stars. In A and F stars an iron-peak convection zone appears. The calculated abundance anomalies are very similar to those observed in AmFm stars in open clusters except that they are larger by a factor of about 3. To reduce the calculated anomalies to the observed ones, an additional source of turbulence (or some other hydrodynamical process) must be introduced. The mixed zone must extend about 5 times deeper than the iron convection zone. Detailed comparisons to a few AmFm stars have been carried out. The LiBeB abundances observed in clusters give additional information. The abundances of the 28 species offer considerable constraints on the models. Various potential turbulence models have been introduced in a stellar evolution code and results of evolutionary calculations for Li gap stars are discussed in the light of the constraints offered by the abundances of LiBeB and metals. The radiative accelerations of LiBeB have also been recalculated taking the effect of changing metal abundances into account. This modifies the expected Li gap in the absence of turbulence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".