THE DATA REDUCTION PIPELINE FOR THE APACHE POINT OBSERVATORY GALACTIC EVOLUTION EXPERIMENT
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 Apache Point Observatory Galactic Evolution Experiment (APOGEE), part of the Sloan Digital Sky Survey III, explores the stellar populations of the Milky Way using the Sloan 2.5-m telescope linked to a high resolution (R ~ 22,500), near-infrared (1.51–1.70 µm) spectrograph with 300 optical fibers. For over 150,000 predominantly red giant branch stars that APOGEE targeted across the Galactic bulge, disks and halo, the collected high signal-to-noise ratio (>100 per half-resolution element) spectra provide accurate (~0.1 km s-1) RVs, stellar atmospheric parameters, and precise (lesssim0.1 dex) chemical abundances for about 15 chemical species. Here we describe the basic APOGEE data reduction software that reduces multiple 3D raw data cubes into calibrated, well-sampled, combined 1D spectra, as implemented for the SDSS-III/APOGEE data releases (DR10, DR11 and DR12). The processing of the near-IR spectral data of APOGEE presents some challenges for reduction, including automated sky subtraction and telluric correction over a 3°-diameter field and the combination of spectrally dithered spectra. We also discuss areas for future improvement.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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