The dependence of galaxy group star formation rates and metallicities on large-scale environment
Bibliographic record
Abstract
We construct a sample of 75 863 star-forming galaxies with robust metallicity and star formation rate (SFR) measurements from the Sloan Digital Sky Survey Data Release 7, from which we select a clean sample of compact group (CG) galaxies. The CGs are defined to be close configurations of at least four galaxies that are otherwise apparently isolated. Our selection results in a sample of 112 spectroscopically identified CG galaxies, which can be further divided into groups that are either embedded within a larger structure, such as a cluster or large group, or truly isolated systems. The CGs then serve as a probe into the influence of large-scale environment on a galaxy’s evolution, while keeping the local density fixed at high values. We find that the SFRs of star-forming galaxies in CGs are significantly different between isolated and embedded systems. Galaxies in isolated systems show significantly enhanced SFR, relative to a control sample matched in mass and redshift, a trend not seen in the embedded systems. Galaxies in isolated systems exhibit a median SFR enhancement at a fixed stellar mass of +0.07 ± 0.03 dex. These dependences on large-scale environment are small in magnitude relative to the apparent influence of local-scale effects found in previous studies, but the significance of the difference in SFRs between our two samples constrains the effect of large-scale environment to be non-zero. We find no significant change in the gas-phase interstellar metallicity for either the isolated or embedded CG sample relative to their controls. However, simulated samples that include artificial offsets indicate that we are only sensitive to metallicity changes of log O/H> 0.13 dex (at 99 per cent confidence), which is considerably larger than the typical metallicity differences seen in previous environmental studies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".