Automated spectroscopic abundances of A and F-type stars using echelle spectrographs
Bibliographic record
Abstract
Using the method presented in Erspamer & North (2002, hereafter Paper I), detailed abundances of 140 stars are presented. The uncertainties characteristic of this method are presented and discussed. In particular, we show that for a $S/N$ ratio higher than 200, the method is applicable to stars with a rotational velocity as high as 200 ${\rm km~s^{-1}}$. There is no correlation between abundances and $V\sin{i}$, except a spurious one for Sr, Sc and Na which we explain by the small number of lines of these elements combined with a locally biased continuum. Metallic giants (Hauck 1986) show larger abundances than normal giants for at least 8 elements: Al, Ca, Ti, Cr, Mn, Fe, Ni and Ba. The anticorrelation for Na, Mg, Si, Ca, Fe and Ni with $V\sin{i}$ suggested by Varenne & Monier (1999) is not confirmed. The predictions of the Montréal models (e.g. Richard et al. 2001) are not fulfilled in general. However, a correlation between $\left[\frac{{\rm Fe}}{{\rm H}}\right]$ and $\log{g}$is found for stars of 1.8 to 2.0 $M_\odot$. Various possible causes are discussed, but the physical reality of this correlation seems inescapable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| 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) | 0.005 | 0.004 |
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".