Non-LTE abundances of Mg and K in extremely metal-poor stars and the evolution of [O/Mg], [Na/Mg], [Al/Mg] and [K/Mg] in the Milky Way.
Bibliographic record
Abstract
LTE abundances of light elements in extremely metal-poor (EMP) stars have been previously derived from high quality spectra. New derivations, free from the NLTE effects, will better constrain the models of the Galactic chemical evolution and the yields of the very first supernovae. The NLTE profiles of the magnesium and potassium lines have been computed in a sample of 53 extremely metal-poor stars with a modified version of the program MULTI and adjusted to the observed lines in order to derive the abundances of these elements. The NLTE corrections for magnesium and potassium are in good agreement with the works found in the literature. The abundances are slightly changed, reaching a better precision: the scatter around the mean of the abundance ratios has decreased. Magnesium may be used with confidence as reference element. Together with previously determined NLTE abundances of sodium and aluminum, the new ratios are displayed, for comparison, along the theoretical trends proposed by some models of the chemical evolution of the Galaxy, using different models of supernovae.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".