ABUNDANCES, STELLAR PARAMETERS, AND SPECTRA FROM THE SDSS-III/APOGEE SURVEY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The SDSS-III/Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey operated from 2011-2014 using the APOGEE spectrograph, which collects high-resolution (R 22,500), near-IR (1.51-1.70 m) spectra with a multiplexing (300 fiber-fed objects) capability. We describe the survey data products that are publicly available, which include catalogs with radial velocity, stellar parameters, and 15 elemental abundances for over 150,000 stars, as well as the more than 500,000 spectra from which these quantities are derived. Calibration relations for the stellar parameters (T eff , g log , [M/H], [/M]) and abundances (C, N, O, Na, Mg, Al, Si, S, K, Ca, Ti, V, Mn, Fe, Ni) are presented and discussed. The internal scatter of the abundances within clusters indicates that abundance precision is generally between 0.05 and 0.09 dex across a broad temperature range; it is smaller for some elemental abundances within more limited ranges and at high signal-to-noise ratio. We assess the accuracy of the abundances using comparison of mean cluster metallicities with literature values, APOGEE observations of the solar spectrum and of Arcturus, comparison of individual star abundances with other measurements, and consideration of the locus of derived parameters and abundances of the entire sample, and find that it is challenging to determine the absolute abundance scale; external accuracy may be good to 0.1-0.2 dex. Uncertainties may be larger at cooler temperatures (T eff 4000 K < ). Access to the public data release and data products is described, and some guidance for using the data products is provided.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it