Chemically Peculiar Stars in the Open Cluster Stock 2
Bibliographic record
Abstract
Abstract The recently rediscovered open cluster Stock 2, located roughly 375 pc away and about 400 Myr old, has the potential to be an exciting new testbed for our understanding of stellar evolution. We present results from a spectroscopic campaign to characterize stars near the cluster’s main-sequence turnoff; our goal is to identify candidate chemically peculiar stars among the cluster’s A stars. We obtained échelle spectra for 64 cluster members with ESPaDOnS on the 3.6 m Canada–France–Hawaii Telescope, Maunakea Observatory, USA, and for six stars with SOPHIE on the 1.93 m telescope at the Observatoire de Haute-Provence, France. We complemented these new observations with those of 13 high-mass cluster members from the HARPS-N archive; our overall sample is of 71 stars. We derived the fundamental parameters (T eff, log g , [M/H]) as well as v e sin i for our sample using the sliced inverse regression technique, and then used iSpec to derive individual abundances of 12 chemical species. With these abundance determinations, we identified nine A stars with anomalous levels of Sc, Ca, and other metallic lines. Follow-up observations of these Am candidates with a known age can transform them into benchmarks for evolutionary models that include atomic diffusion and help build a better understanding of the complex interactions between macroscopic and microscopic processes in stellar interiors.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".