Bibliographic record
Abstract
Context. It is becoming clear that determination of the abundance of Si using lines of Si ii and Si iii can lead to quite discordant results in mid to late B-type stars. The difference between the Si abundances derived from the two ion states can exceed one dex in some cases. Aims. We have carried out a study intended to clarify which kinds of B stars exhibit this discrepancy, to try to identify regularities in the phenomenon, and to explore possible explanations such as abundance stratification by comparing models to observed spectra. Methods. We used spectra from the ESPaDOnS spectropolarimeter and FEROS spectrograph, supplemented with spectra from the ESO and ELODIE archives, of magnetic Bp, HgMn, and normal B-type stars ranging in effective temperature from about 10 500 to 15 000 K. Using these spectra, we derived abundances using the spectrum synthesis program zeeman, which can take the influence of magnetic fields into account. For each star, accurate abundances of Si ii, Si iii, Ti, Cr, and Fe were derived from two separate ~100 Å windows. Si ii abundances were deduced from multiple lines, and Si iii abundances were found using λλ 4552, 4567, and 4574. Results. All magnetic Bp stars in our sample show a discordance between the derived abundances of the first and second ions of silicon, with the latter being between 0.6−1.7 dex higher. The same behaviour is observed in the non-magnetic stars but to a much smaller extent: Si iii is enhanced by between 0.3−0.8 dex compared to Si ii. We do not detect the discrepancy in three stars, HD 22 136 (normal), HD 57 608 (HgMn) and HD 27 295 (HgMn); these are the only stars in our sample for which the microturbulence parameter is significantly different from zero, and which therefore probably have convection occurring in their atmospheres. Conclusions. We find that vertical stratification of silicon in the atmospheres of B-type stars may provide an explanation of this phenomenon, but our detailed stratification models do not completely explain the discrepancies, which may, in part, be due to non-LTE effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".