NLTE Strontium and Barium in Metal‐poor Red Giant Stars
Bibliographic record
Abstract
We present atmospheric models of red giant stars of various metallicities, including extremely metal poor (XMP; [Fe/H] < -3.5) models, with many chemical species, including, significantly, the first two ionization stages of strontium (Sr) and barium (Ba), treated in non-local thermodynamic equilibrium (NLTE) with various degrees of realism. We conclude that (1) for all lines that are useful Sr and Ba abundance diagnostics, the magnitude and sense of the computed NLTE effect on the predicted line strength is metallicity dependent, (2) the indirect NLTE effect of overlap between Ba and Sr transitions and transitions of other species that are also treated in NLTE nonnegligibly enhances NLTE abundance corrections for some lines, (3) the indirect NLTE effect of NLTE opacity of other species on the equilibrium structure of the atmospheric model is not significant, (4) the computed NLTE line strengths differ negligibly if collisional b-b and b-f rates are an order of magnitude smaller or larger than those calculated with standard analytic formulae, and (5) the effect of NLTE on the resonance line of Ba II at 4554.03 Å is independent of whether that line is treated with hyperfine splitting. As a result, the derivation of abundances of Ba and Sr for metal-poor red giant stars with LTE modeling that are in the literature should be treated with caution.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".