Open clusters in APOGEE and GALAH
Bibliographic record
Abstract
Context . Open clusters are ideal laboratories to investigate a variety of astrophysical topics, from the properties of the Galactic disc to stellar-evolution models. Knowing their metallicity and possibly detailed chemical abundances is therefore important. However, the number of systems with chemical abundances determined from high-resolution spectroscopy remains small. Aims. Our aim is to increase the number of open clusters with radial velocities and chemical abundances determined from high-resolution spectroscopy using publicly available catalogues of surveys in combination with Gaia data. Methods . Open cluster stars have been identified in the APOGEE and GALAH spectroscopic surveys by cross-matching their latest data releases with stars for which high-probability astrometric membership has been derived in many clusters on the basis of the Gaia second data release. Results . Radial velocities were determined for 131 and 14 clusters from APOGEE and GALAH data, respectively. This is the first radial-velocity determination from high-resolution spectra for 16 systems. Iron abundances were obtained for 90 and 14 systems from APOGEE and GALAH samples, respectively. To our knowledge 66 of these clusters (57 in APOGEE and 9 in GALAH) do not have previous determinations in the literature. For 90 and 7 clusters in the APOGEE and GALAH samples, respectively, we also determined average abundances for Na, Mg, Al, Si, Ca, Cr, Mn, and Ni.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".