A STUDY OF THE ELEMENTS COPPER THROUGH URANIUM IN SIRIUS A: CONTRIBUTIONS FROM STIS AND GROUND-BASED SPECTRA
Bibliographic record
Abstract
ABSTRACT We determine abundances or upper limits for all of the 55 stable elements from copper to uranium for the A1 Vm star Sirius. The purpose of the study is to assemble the most complete picture of elemental abundances with the hope of revealing the chemical history of the brightest star in the sky, apart from the Sun. We also explore the relationship of this hot metallic-line (Am) star to its cooler congeners, as well as the hotter, weakly- or non-magnetic Mercury-manganese (HgMn) stars. Our primary observational material consists of Hubble Space Telescope (HST) spectra taken with the Space Telescope Imaging Spectrograph in the ASTRAL project. We have also used archival material from the COPERNICUS satellite, and from the HST Goddard High-Resolution Spectrograph, as well as ground-based spectra from Furenlid, Westin, Kurucz, Wahlgren, and their coworkers, ESO spectra from the UVESPOP project, and NARVAL spectra retrieved from PolarBase. Our analysis has been primarily by spectral synthesis, and in this work we have had the great advantage of extensive atomic data unavailable to earlier workers. We find most abundances as well as upper limits range from 10 to 100 times above solar values. We see no indication of the huge abundance excesses of 1000 or more that occur among many chemically peculiar stars of the upper main sequence. The picture of Sirius as a hot Am star is reinforced.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".