THE GALAXY POPULATION OF LOW-REDSHIFT ABELL CLUSTERS
Bibliographic record
Abstract
We present a study of the luminosity and color properties of galaxies selected from a sample of 57 low-redshift Abell clusters. We utilize the non-parametric dwarf-to-giant ratio (DGR) and the blue galaxy fraction ( f b ) to investigate the clustercentric radial-dependent changes in the cluster galaxy population. Composite cluster samples are combined by scaling the counting radius by r 200 to minimize radius selection bias. The separation of galaxies into a red and blue population was achieved by selecting galaxies relative to the cluster color–magnitude relation. The DGR of the red and blue galaxies is found to be independent of cluster richness ( B gc ), although the DGR is larger for the blue population at all measured radii. A decrease in the DGR for the red and red+blue galaxies is detected in the cluster core region, while the blue galaxy DGR is nearly independent of radius. The f b is found not to correlate with B gc ; however, a steady decline toward the inner-cluster region is observed for the giant galaxies. The dwarf galaxy f b is approximately constant with clustercentric radius except for the inner-cluster core region where f b decreases. The clustercentric radial dependence of the DGR and the galaxy blue fraction indicates that it is unlikely that a simple scenario based on either pure disruption or pure fading/reddening can describe the evolution of infalling dwarf galaxies; both outcomes are produced by the cluster environment.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".