Galaxy Evolution & The Mass-Size Relation In Z~1 Clusters
Bibliographic record
Abstract
Galaxies residing in high-density environments are preferentially red with early-type morphology. This has long since implied differences in the way cluster and field galaxies evolve. Nevertheless, there remains disagreement in the literature on how the cluster environment influences the structural evolution of galaxies. The stellar mass-size relation for galaxies has proven to be a powerful tool in understanding how galaxies grow. Recent results in the field environment show two distinct relations star-forming and quiescent galaxies follow for a broad range of redshifts. Clusters provide an opportunity to measure this mass-size relation in a much higher density environment, allowing us to probe environmental influences on galaxy evolution. For example, it is hypothesised that minor mergers drive the evolution of the mass-size relation; however, such mergers are uncommon in clusters, making them a key region to test this hypothesis. I will present some of the first results on the cluster mass-size relation from HST follow up to GCLASS. GCLASS is the largest spectroscopic survey conducted on 10 clusters at z~1. Our data has increased the sample of cluster galaxies with accurate size measurements, allowing us to find the first clear distinctions between the cluster and field mass-size relations at z~1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".