The NIR Ca ii triplet at low metallicity
Bibliographic record
Abstract
The NIR Ca ii triplet absorption lines have proven to be an important tool for quantitative spectroscopy of individual red giant branch stars in the Local Group, providing a better understanding of metallicities of stars in the Milky Way and dwarf galaxies and thereby an opportunity to constrain their chemical evolution processes. An interesting puzzle in this field is the significant lack of extremely metal-poor stars, below [Fe/H] = –3, found in classical dwarf galaxies around the Milky Way using this technique. The question arises whether these stars are really absent, or if the empirical Ca ii triplet method used to study these systems is biased in the low-metallicity regime. Here we present results of synthetic spectral analysis of the Ca ii triplet, that is focused on a better understanding of spectroscopic measurements of low-metallicity giant stars. Our results start to deviate strongly from the widely-used and linear empirical calibrations at [Fe/H] < –2. We provide a new calibration for Ca ii triplet studies which is valid for –0.5 ≥ [Fe/H] ≥ –4. We subsequently apply this new calibration to current data sets and suggest that the classical dwarf galaxies are not so devoid of extremely low-metallicity stars as was previously thought.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".