The two metallicity groups of the globular cluster M 22: a chemical perspective
Bibliographic record
Abstract
We present a detailed chemical composition analysis of 35 red giant stars in the globular cluster M 22. High resolution spectra for this study were obtained at five observatories, and analyzed in a uniform manner. We have determined abundances of representative light proton-capture, α, Fe-peak and neutron-capture element groups. Our aim is to better understand the peculiar chemical enrichment history of this cluster, in which two stellar groups are characterized by a different content in iron, neutron capture elements Y, Zr and Ba, and α element Ca. The principal results of this study are: (i) substantial star-to-star metallicity scatter (− 2.0 ≲ [Fe/H] ≲ −1.6); (ii) enhancement of s-process/r-process neutron-capture abundance ratios in a fraction of giants, positively correlated with metallicity; (iii) sharp separation between the s-process-rich and s-process-poor groups by [La/Eu] ratio; (iv) possible increase of [Cu/Fe] ratios with increasing [Fe/H], suggesting that this element also has a significant s-process component; and (v)presence of Na-O and C-N anticorrelations in both the stellar groups.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".