A Spectroscopic Classification Survey to Search for New ρ Puppis Stars
Bibliographic record
Abstract
Abstract The ρ Puppis stars are mid-F-type stars that show peculiar chemical-abundance patterns similar to those of the Am stars. Typically they exhibit overabundances of iron-peak elements such as Fe and Ni and s- and r-process elements such as Sr and Eu, and underabundances of certain other elements including He, Ca, and Sc. It has been proposed that these stars are evolving Am stars passing through the short-lived phase that occurs between the re-establishment of convection and the consequent erasure of their chemical peculiarities. In this paper we suggest a second hypothesis: these stars may have acquired their peculiar abundance patterns in a fashion similar to the Barium stars, i.e., they may have gained the s-process element enhancements via mass transfer from a once asymptotic giant-branch companion star, now turned white dwarf. This study will detail our efforts to investigate the ρ Puppis stars with regard to these two hypotheses, with a view to understanding these stars and their significance in terms of stellar evolution. We have performed a spectral classification survey, and a detailed chemical-abundance analysis of selected ρ Puppis stars. This paper gives an overview of the ρ Puppis stars and describes the spectral classification survey which has increased the number of ρ Puppis stars currently known from 6 to 49. A future paper will describe the chemical-abundance analysis which should provide insight into the nature of these 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| 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 | 0.001 |
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".