The Evolution of AmFm Stars, Abundance Anomalies, and Turbulent Transport
Bibliographic record
Abstract
Stellar evolution models of stars of 1.45-3.0 M ☉ have been calculated, including the atomic diffusion of metals and radiative accelerations for all species in the OPAL opacities. As the abundances change, the opacities and radiative accelerations are continuously recalculated during evolution. These models develop iron-peak convection zones centered at a temperature of approximately 200,000 K. If one then assumes that there is sufficient overshoot to homogenize the surface regions between the hydrogen, helium, and iron-peak convection zones, it is shown here that the surface abundance variations that are produced, without any arbitrary parameter, closely resemble the abundance anomalies of AmFm stars, except that they are larger by a factor of about 3. Detailed evolutionary model calculations have been carried out, varying the turbulence in the outer stellar regions in order to improve the agreement with the observed anomalies in AmFm stars. The outer mass mixed by turbulence has been varied, as well as the density dependence of the turbulent diffusion coefficient. It is shown that the anomalies depend on only one parameter characterizing turbulence, namely, the depth of the zone mixed by turbulence. The calculated surface abundances are compared to observations of a number of recently observed AmFm stars. For Sirius A, 16 abundances (including four upper limits) are available for comparison. Of these, 12 are well reproduced by the model, while three are not so well reproduced, and one is a very uncertain observation. In cluster AmFm stars, the age and initial abundances are known. There is then less arbitrariness in the calculations, but fewer chemical species have been observed than in Sirius. The available observations (Hyades, Pleiades, and Praesepe stars are compared) agree reasonably well with the calculated models for the five stars that are compared. The zone mixed by turbulence is deeper than the iron convection zone, reducing the abundance anomalies to values that are too small for iron-peak convection zones to develop in many of the models. The origin of the mixing process then remains uncertain. There is considerable scatter in the observations between different observers, so it is premature to conclude that hydrodynamical processes other than turbulence are needed to explain the observations. We do not rule out the possibility that this might be the case, but the observations do not appear to us to be good enough to establish it.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".