The GOGREEN and GCLASS surveys: first data release
Bibliographic record
Abstract
ABSTRACT We present the first public data release of the GOGREEN (Gemini Observations of Galaxies in Rich Early Environments) and GCLASS (Gemini CLuster Astrophysics Spectroscopic Survey) surveys of galaxies in dense environments, spanning a redshift range 0.8 < z < 1.5. The surveys consist of deep, multiwavelength photometry and extensive Gemini GMOS spectroscopy of galaxies in 26 overdense systems ranging in halo mass from small groups to the most massive clusters. The objective of both projects was primarily to understand how the evolution of galaxies is affected by their environment, and to determine the physical processes that lead to the quenching of star formation. There was an emphasis on obtaining unbiased spectroscopy over a wide stellar mass range (M ≳ 2 × 1010 M⊙), throughout and beyond the cluster virialized regions. The final spectroscopic sample includes 2771 unique objects, of which 2257 have reliable spectroscopic redshifts. Of these, 1704 have redshifts in the range 0.8 < z < 1.5, and nearly 800 are confirmed cluster members. Imaging spans the full optical and near-infrared wavelength range, at depths comparable to the UltraVISTA survey, and includes Hubble Space Telescope/Wide Field Camera 3 F160W (GOGREEN) and F140W (GCLASS). This data release includes fully reduced images and spectra, with catalogues of advanced data products including redshifts, line strengths, star formation rates, stellar masses, and rest-frame colours. Here, we present an overview of the data, including an analysis of the spectroscopic completeness and redshift quality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.026 |
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".