Mass-segregation trends in SDSS galaxy groups
Bibliographic record
Abstract
Abstract It has been shown that galaxy properties depend strongly on their host environment. In order to understand the relevant physical processes driving galaxy evolution it is important to study the observed properties of galaxies in different environments. Mass segregation in bound galaxy structures is an important indicator of evolutionary history and dynamical friction time-scales. Using group catalogues derived from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7), we investigate mass-segregation trends in galaxy groups at low redshift. We investigate average galaxy stellar mass as a function of group-centric radius and find evidence for weak mass segregation in SDSS groups. The magnitude of the mass segregation depends on both galaxy stellar mass limits and group halo mass. We show that the inclusion of low-mass galaxies tends to strengthen mass-segregation trends, and that the strength of mass segregation tends to decrease with increasing group halo mass. We find the same trends if we use the fraction of massive galaxies as a function of group-centric radius as an alternative probe of mass segregation. The magnitude of mass segregation that we measure, particularly in high-mass haloes, indicates that dynamical friction is not acting efficiently.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| 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) | 0.001 | 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".