Baade’s window and APOGEE
Bibliographic record
Abstract
Context.Baade’s window (BW) is one of the most observed Galactic bulge fields in terms of chemical abundances. Owing to its low and homogeneous interstellar absorption it is considered the perfect calibration field for Galactic bulge studies. Aims.In the era of large spectroscopic surveys, calibration fields such as BW are necessary for cross calibrating the stellar parameters and individual abundances of the APOGEE survey. Methods.We use the APOGEE BW stars to derive the metallicity distribution function (MDF) and individual abundances forα- and iron-peak elements of the APOGEE ASPCAP pipeline (DR13), as well as the age distribution for stars in BW. Results.We determine the MDF of APOGEE stars in BW and find a remarkable agreement with that of theGaia-ESO survey (GES). Both exhibit a clear bimodal distribution. We also find that the Mg-metallicity planes of the two surveys agree well, except for the metal-rich part ([Fe/H] > 0.1), where APOGEE finds systematically higher Mg abundances with respect to the GES. The ages based on the [C/N] ratio reveal a bimodal age distribution, with a major old population at ~ 10 Gyr, with a decreasing tail towards younger stars. A comparison of stellar parameters determined by APOGEE and those determined by other sources reveals detectable systematic offsets, in particular for spectroscopic surface gravity estimates. In general, we find a good agreement between individual abundances of O, Na, Mg, Al, Si, K, Ca, Cr, Mn, Co, and Ni from APOGEE with that of literature values. Conclusions.We have shown that in general APOGEE data show a good agreement in terms of MDF and individual chemical abundances with respect to literature works. Using the [C/N] ratio we found a significant fraction of young stars in BW.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".