A SHARK's view of the galaxy-AGN-environment connection across cosmic time
Bibliographic record
Abstract
Both environment and AGN play important roles in shaping the evolution of galaxies, though the relative importance of these mechanisms remains an area of active research. Less explored has been how the galaxy-AGN and galaxy-environment connections impact each other due to current observational limitations. The combination of large surveys from upcoming observational facilities, both space-based (e.g., Athena, CASTOR, Roman) and ground-based (e.g., 4MOST, PFS, Rubin), promises to greatly expand the current range of measured redshift, environments, and AGN/galaxy properties. These surveys will not only enable a deeper understanding of the galaxy-environment and galaxy-AGN connections but also offer a view into the overall galaxy-AGN-environment interplay. Theoretical predictions for the evolution of the galaxy-AGN-environment scaling relations will allow us to best exploit the results from these surveys. In this talk, I will introduce SHARK v2.0 semi-analytic model, with a special focus on the new AGN models implemented, and explore the resulting predictions for galaxy/SMBH/AGN properties. I will compare these predictions against current observations and conclude with our view of what we expect the upcoming surveys will unveil.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".