Lithium abundances as a tracer of asymptotic giant branch star pollution in the globular cluster NGC 6752
Bibliographic record
Abstract
ABSTRACT This paper presents a chemical abundance analysis of 217 stars in the metal-poor globular cluster NGC 6752, distributed from the turn-off to the lower red giant branch. Al and Li abundances were derived through spectral synthesis applied to spectra collected with FLAMES, in both GIRAFFE and UVES modes. The work aims to gain insight into the nature of the polluter(s) responsible for the abundance variations and the C-N, Na-O, Al-Mg anticorrelations associated with the multiple-population phenomenon. We found a plateau at A(Li) = 2.33 ± 0.06 dex in unevolved stars, with the average Li content decreasing continuously down to ∼1.25 dex at the bottom of the red giant branch. As expected in the classic anticorrelation scenario, we found stars low in Al and high in Li abundance, and stars high in Al and low in Li. However, in addition, we also found evidence of Al-rich, second-generation stars with high Li content. This finding suggests the need for Li production, known to happen in intermediate-mass (∼4–8 M⊙) asymptotic giant branch stars through the Cameron–Fowler mechanism. It is worth noting that the Li abundance observed in Al-rich stars never exceeds that in Al-poor stars.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".