The Third Data Release of the Beijing–Arizona Sky Survey
Bibliographic record
Abstract
Abstract The Beijing–Arizona Sky Survey (BASS) is a wide and deep imaging survey that covers a 5400 deg2 area in the northern Galactic cap with the 2.3 m Bok telescope using two filters (g and r bands). The Mosaic z-band Legacy Survey (MzLS) covers the same area in the z band with the 4 m Mayall telescope. These two surveys will be used for spectroscopic targeting by the Dark Energy Spectroscopic Instrument (DESI) in the northernmost portion of the DESI footprint. This paper describes the third data release (DR3) of BASS, which contains the photometric data from all BASS and MzLS observations from 2015 January until the completion of BASS in 2019 March. The median astrometric precision relative to Gaia positions is about 17 mas and the median photometric offset relative to the Pan-STARRS1 photometry is within 5 mmag. The median 5σ AB magnitude depths for point sources are 24.2, 23.6, and 23.0 mag for the g, r, and z bands, respectively. The photometric depth within the survey area is highly homogeneous, and the difference between the 20% and 80% depth is less than 0.3 mag. The DR3 data, including raw data, calibrated single-epoch images, single-epoch photometric catalogs, stacked images, and co-added photometric catalogs, are publicly accessible at http://batc.bao.ac.cn/BASS/doku.php?id=datarelease:home .
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.022 |
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".