The Sloan Digital Sky Survey Quasar Catalog. III. Third Data Release
Bibliographic record
Abstract
We present the third edition of the Sloan Digital Sky Survey (SDSS) Quasar Catalog. The catalog consists of the 46,420 objects in the SDSS Third Data Release that have luminosities larger than M i = -22 (in a cosmology with H 0 = 70 km s -1 Mpc -1 , Ω M = 0.3, and Ω Λ = 0.7), have at least one emission line with FWHM larger than 1000 km s -1 or are unambiguously broad absorption line quasars, are fainter than i = 15.0, and have highly reliable redshifts. The area covered by the catalog is ≈4188 deg 2 . The quasar redshifts range from 0.08 to 5.41, with a median value of 1.47; the high-redshift sample includes 520 quasars at redshifts greater than 4, of which 17 are at redshifts greater than 5. For each object the catalog presents positions accurate to better than 0 2 rms per coordinate, five-band ( ugriz ) CCD-based photometry with typical accuracy of 0.03 mag, and information on the morphology and selection method. The catalog also contains radio, near-infrared, and X-ray emission properties of the quasars, when available, from other large-area surveys. The calibrated digital spectra cover the wavelength region 3800–9200 Å at a spectral resolution of ≃2000; the spectra can be retrieved from the public database using the information provided in the catalog. A total of 44,221 objects in the catalog were discovered by the SDSS; 28,400 of the SDSS discoveries are reported here for the first time.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.105 |
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".