Investigation of a sample of carbon-enhanced metal-poor stars observed with FORS and GMOS
Bibliographic record
Abstract
Aims: Carbon-enhanced metal-poor (CEMP) stars represent a sizeable fraction of all known metal-poor stars in the Galaxy. Their formation and composition remains a significant topic of investigation within the stellar astrophysics community. Methods: We analysed a sample of low-resolution spectra of 30 dwarf stars, obtained using the visual and near UV FOcal Reducer and low dispersion Spectrograph for the Very Large Telescope (FORS/VLT) of the European Southern Observatory (ESO) and the Gemini Multi-Object Spectrographs (GMOS) at the GEMINI telescope, to derive their metallicity and carbon abundance. Results: We derived C and Ca from all spectra, and Fe and Ba from the majority of the stars. Conclusions: We have extended the population statistics of CEMP stars and have confirmed that in general, stars with a high C abundance belonging to the high C band show a high Ba-content (CEMP-s or -r/s), while stars with a normal C abundance or that are C-rich, but belong to the low C band, are normal in Ba (CEMP- no). Based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme ID 099.D-0791.Based on observations obtained at the Gemini Observatory (processed using the Gemini IRAF package), which is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (United States), the National Research Council (Canada), CONICYT (Chile), Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina), and Ministério da Ciência, Tecnologia e Inovação (Brazil).Tables 1 and 2 are also available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz- bin/qcat?J/A+A/614/A68
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".