Bibliographic record
Abstract
The study of chemical abundances in stellar atmosphere provides a useful tool to investigate the formation and evolution history of stars. The optical wavelength range has been used almost exclusively in the past to determine the elemental abundance in A-type stars. We use high-resolution, high signal-to-noise ultraviolet spectra obtained from the STIS/NUV-MAMA instrument on board Hubble Space Telescope. The spectra available cover the wavelength ranges 1630 Å–1901 Å and 2130 Å–2887 Å. The main challenge to carrying out abundance analysis in the ultraviolet is the extreme level of line blending. Abundance analysis using single isolated spectral lines is almost completely impossible; it is necessary to model spectral windows using spectrum synthesis with fairly complete line-lists. We have used the LTE spectrum synthesis code zeeman to model the UV spectrum of HD 72660, adjusting abundances for a best match for elements with 6 ≤ Z≤ 82 for which lines are present in the Vinna Atomic Line Database line-list. Abundances or upper limits are derived for 32 elements. We find that except a few, our derived abundances are slightly higher than solar values. We estimate upper limits for abundances of eleven elements and abundance values of 12 elements which have not been detected in the optical. The high abundances that we find for some heavy elements may point to radiative levitation. The presence of lanthanides plus our results, suggest the reclassification of HD 72660 as a transition object between an HgMn star and an Am star.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".