Chemical Abundances along the 1G Sequence of the Chromosome Maps: The Globular Cluster NGC 3201*
Bibliographic record
Abstract
Abstract The Hubble Space Telescope (HST) UV Legacy Survey of Galactic Globular Clusters (GCs) has investigated multiple stellar populations by means of the “chromosome map” (ChM) diagnostic tool that maximizes the separation between stars with different chemical compositions. One of the most challenging features revealed by ChM analysis is the apparent inhomogeneity among stars belonging to the first population, a phenomenon largely attributed to He variations. However, this explanation is not supported by uniformity in the p-capture elements of these stars. The HST survey has revealed that the GC NGC 3201 shows exceptionally wide coverage in the parameter of the ChM. We present a chemical abundance analysis of 24 elements in 18 giants belonging to the first population of this GC and having a wide range in . As far as the p-capture elements are concerned, the chemical abundances are typical of first-generation (1G) stars, as expected from the location of our targets in the ChM. Based on radial velocities and chemical abundance arguments, we find that the three stars with the lowest values are binary candidates. This suggests that at least those stars could be explained with binarity. These results are consistent with evidence inferred from multiband photometry that evolved blue stragglers (BSs) populate the bluest part of the 1G sequence in the ChM. The remaining 15 spectroscopic targets show a small range in the overall metallicity by ∼0.10 dex, with stars at higher values having higher absolute abundances. We suggest that a small variation in metals and binarity governs the color spread of the 1G in the ChM and that evolved BSs contribute to the bluest tail of the 1G sequence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".