THE SLOAN DIGITAL SKY SURVEY QUASAR LENS SEARCH. II. STATISTICAL LENS SAMPLE FROM THE THIRD DATA RELEASE
Bibliographic record
Abstract
We report the first results of our systematic search for strongly lensed quasars using the spectroscopically confirmed quasars in the Sloan Digital Sky Survey (SDSS). Among 46,420 quasars from the SDSS Data Release 3 (∼4188 deg 2 ), we select a subsample of 22,683 quasars that are located at redshifts between 0.6 and 2.2 and are brighter than the Galactic extinction-corrected i -band magnitude of 19.1. We identify 220 lens candidates from the quasar subsample, for which we conduct extensive and systematic follow-up observations in optical and near-infrared wavebands, in order to construct a complete lensed quasar sample at image separations between 1'' and 20'' and flux ratios of faint to bright lensed images larger than 10 −0.5 . We construct a statistical sample of 11 lensed quasars. Ten of these are galaxy-scale lenses with small image separations (∼ 1''–2'') and one is a large separation (15'') system which is produced by a massive cluster of galaxies, representing the first statistical sample of lensed quasars including both galaxy- and cluster-scale lenses. The Data Release 3 spectroscopic quasars contain an additional 11 lensed quasars outside the statistical sample.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".