Elemental Abundance Analyses with DAO Spectrograms. XXXV. On the Iron Abundances of B and A Stars
Bibliographic record
Abstract
I compared the results of LTE fine analyses for B and A stars based on the newer "precise and accurate" Fe II gf values of Melendez & Barbuy (MB) with those based on the relatively recent major critical compilation of Fuhr & Wiese (FW). Only nonblended Fe II lines with both FW and MB gf values for 34 B and A stars with equivalent widths derived from high dispersion, high (>200) signal-to-noise ratio Dominion Astrophysical Observatory spectra were used. For most stars the standard deviations of the abundances derived from Fe II lines decrease slightly with the MB values, which is the signature of better consistency among the gf values. Then, for stars with many Fe I lines, I performed analyses using all lines with FW gf values and those with only A and B quality gf values and found minor improvements in the latter case. However, the abundances and microturbulences derived from Fe I lines are in better agreement with the Fe II FW results. The discrepancy between the results for Fe I FW and Fe II MB values could be due to NLTE effects on Fe I. A more limited comparison is made with the recent theoretical values of Deb & Hibbert which, when used, show a greater scatter of the derived Fe II gf values and smaller abundances than those obtained with the MB gf values.
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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 | 0.004 | 0.002 |
| 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 | 0.005 |
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".