The Evolution of Galaxies and Groups in Cluster Environments at 0.3 < <i>z</i> < 0.6
Bibliographic record
Abstract
We investigate the dependence of galaxy populations on environment. Our samples are selected from the follow-up of Red-Sequence Cluster Survey (RCS) catalogs using wide-field BVRz' imaging for 60 intermediate redshift (0.3 < z < 0.6) clusters. Galaxy redshifts are estimated using an empirical photometric redshift technique with a training set of 3996 galaxies to z 1.4. To obtain photometric redshift probability density for each galaxy, we bootstrap the training set galaxies to estimate the fitting uncertainties and apply Monte-Carlo method to simulate galaxy magnitudes errors. In order to find galaxy groups using photometric redshift, we develop a modified friends-of-friends algorithm, ‘Probability Friends-of-Friends Algorithm (pFOF)’, where photometric redshift redshift probability densities of individual galaxies are used to determine member galaxies of a group. We calculate the red galaxy fraction to infer the evolutionary status of cluster galaxies and also for galaxies in groups selected in the same redshift space as the clusters.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".