The Carina Project
Bibliographic record
Abstract
We have performed a new abundance analysis of Carina red giant (RG) stars from spectroscopic data collected with UVES (high spectral resolution) and FLAMES/GIRAFFE (high and medium resolution) at ESO/VLT. The former sample includes 44 RGs, while the latter consists of 65 (high-resolution) and ~800 (medium-resolution) RGs, covering a significant fraction of the galaxy’s RG branch, and red clump stars. To improve the abundance analysis at the faint magnitude limit, the FLAMES/GIRAFFE data were divided into ten surface gravity and effective temperature bins. The spectra of the stars belonging to the same gravity and temperature bin were stacked. This approach allowed us to increase the signal-to-noise ratio in the faint magnitude limit (V≥ 20.5 mag) by at least a factor of five. We took advantage of the new photometry index cU,B,I introduced recently as an age and probably a metallicity indicator to split stars along the red giant branch. These two stellar populations display distinct [Fe/H] and [Mg/H] distributions: their mean iron abundances are −2.15 ± 0.06 dex (σ = 0.28), and −1.75 ± 0.03 dex (σ = 0.21), respectively. The two iron distributions differ at the 75% level. This supports preliminary results. Moreover, we found that the old and intermediate-age stellar populations have mean [Mg/H] abundances of −1.91 ± 0.05 dex (σ = 0.22) and −1.35 ± 0.03 dex (σ = 0.22); these differ at the 83% level. Carina’s α-element abundances agree, within 1σ, with similar abundances for field halo stars and for cluster (Galactic and Magellanic) stars. The same outcome applies to nearby dwarf spheroidals and ultra-faint dwarf galaxies in the iron range covered by Carina stars. Finally, we found evidence of a clear correlation between Na and O abundances, thus suggesting that Carina’s chemical enrichment history is quite different from that in the globular clusters.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.118 | 0.098 |
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".