Environments of active galactic nuclei in the close active galactic nuclei reference survey
Bibliographic record
Abstract
Investigating the environments of galaxies and active galactic nuclei (AGN) is crucial to understanding the formation and evolution of AGN and the host galaxies in which they reside. The environments of galaxies can inform star formation and morphology. The environments of AGN can inform possible accretion scenarios and therefore activity, as well as feedback. Similar environments are expected for the various flavours of AGN as guided by AGN unification. As well, if active galaxies reside in the same environments as passive galaxies, this supports normal galaxies experiencing relatively short periods of activity throughout their lifetimes. We have characterized the environments of a sample of nearby Type I AGN as well as the environments of other active and passive galaxies in the same redshift range. We find a strong similarity between the environments of the reference sample with the comparison AGN and normal galaxies. These results support AGN unification and AGN/galaxy unification where normal galaxies have active periods. We then investigate the environmental dependence for some AGN and host galaxy properties. We find no strong dependence for any parameter over both small scale environment and larger scale environment. This suggests that galaxy and AGN evolution is not sensitive to environment, but is driven by processes within the host galaxy for our sample of optically selected nearby Type I AGN.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".