The Sixth Data Release of the Sloan Digital Sky Survey
Bibliographic record
Abstract
This paper describes the Sixth Data Release of the Sloan Digital Sky Survey. With this data release, the imaging of the northern Galactic cap is now complete. The survey contains images and parameters of roughly 287 million objects over 9583 deg 2 , including scans over a large range of Galactic latitudes and longitudes. The survey also includes 1.27 million spectra of stars, galaxies, quasars, and blank sky (for sky subtraction) selected over 7425 deg 2 . This release includes much more stellar spectroscopy than was available in previous data releases and also includes detailed estimates of stellar temperatures, gravities, and metallicities. The results of improved photometric calibration are now available, with uncertainties of roughly 1% in g , r , i , and z , and 2% in u , substantially better than the uncertainties in previous data releases. The spectra in this data release have improved wavelength and flux calibration, especially in the extreme blue and extreme red, leading to the qualitatively better determination of stellar types and radial velocities. The spectrophotometric fluxes are now tied to point-spread function magnitudes of stars rather than fiber magnitudes. This gives more robust results in the presence of seeing variations, but also implies a change in the spectrophotometric scale, which is now brighter by roughly 0.35 mag. Systematic errors in the velocity dispersions of galaxies have been fixed, and the results of two independent codes for determining spectral classifications and redshifts are made available. Additional spectral outputs are made available, including calibrated spectra from individual 15 minute exposures and the sky spectrum subtracted from each exposure. We also quantify a recently recognized underestimation of the brightnesses of galaxies of large angular extent due to poor sky subtraction; the bias can exceed 0.2 mag for galaxies brighter than r = 14 mag.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.071 |
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".