A Search for Nitrogen‐enhanced Metal‐poor Stars
Bibliographic record
Abstract
Theoretical models of very metal-poor intermediate-mass asymptotic giant branch (AGB) stars predict a large overabundance of primary nitrogen. The very metal-poor, carbon-enhanced, s -process-rich stars, which are thought to be the polluted companions of now extinct AGB stars, provide direct tests of the predictions of these models. Recent studies of the carbon and nitrogen abundances in metal-poor stars have focused on the most carbon-rich stars, leading to a potential selection bias against stars that have been polluted by AGB stars that produced large amounts of nitrogen and hence have small [C/N] ratios. We call these stars nitrogen-enhanced metal-poor (NEMP) stars and define them as having [N/Fe] > +0.5 and [C/N] < -0.5. In this paper we report on the [C/N] abundances of a sample of 21 carbon-enhanced stars, all but three of which have [C/Fe] < +2.0. If NEMP stars were made as easily as carbon-enhanced metal-poor (CEMP) stars, then we expected to find between two and seven NEMP stars. Instead, we found no NEMP stars in our sample. Therefore, this observational bias is not an important contributor to the apparent dearth of N-rich stars. Our [C/N] values are in the same range as values reported previously in the literature (-0.5 to +2.0), and all stars are in disagreement with the predicted [C/N] ratios for both low- and high-mass AGB stars. We suggest that the decrease in [C/N] from the low-mass AGB models is due to enhanced extramixing, while the lack of NEMP stars may be caused by unfavorable mass ratios in binaries or the difficulty of mass transfer in binary systems with large mass ratios.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".