MétaCan
Menu
Back to cohort

The Environmental Dependence of the Age of Active Galaxies and the Dependence of the Clustering Properties of Active Galaxies on Age

2020· article· en· W3017101812 on OpenAlexfundno aff
Xin‐Fa Deng, Xiaoqing Wen

Bibliographic record

VenueRevista Mexicana de Astronomía y Astrofísica · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryCollege of Engineering, Michigan State UniversityNational Natural Science Foundation of ChinaYork UniversityJohns Hopkins UniversityOhio State UniversityNew Mexico State UniversityVanderbilt UniversityYale UniversityUniversity of PortsmouthNational Science FoundationUniversity of WashingtonAlfred P. Sloan FoundationUniversity of ArizonaPrinceton UniversityBrookhaven National LaboratoryU.S. Department of Energy
KeywordsPhysicsAstrophysicsActive galactic nucleusGalaxy groupGalaxyLuminous infrared galaxyBrightest cluster galaxyAstronomy

Abstract

fetched live from OpenAlex

We explore the environmental dependence of the age and the clustering-age dependence of active galaxies in two volume-limited active galaxy samples of the SDSS. In the luminous volume-limited sample, the age of active galaxies apparently depends on environments: at low density the fraction of young galaxies with ages less than 2 Gyr is significantly higher than the one in the high density case, at high density the fraction of old galaxies is significantly higher than the one at low density. Young active galaxies have a higher prevalence than old ones at small multiplicity, corresponding to isolated galaxies, close pairs and small groups, whereas old active galaxies have a higher prevalence in the multiplicity bins corresponding to dense groups and clusters. But in the faint volume-limited sample, although the environmental dependence of the age is fairly weak, a substantial clustering-age dependence can be observed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.195
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRevista Mexicana de Astronomía y AstrofísicaSame topicGalaxies: Formation, Evolution, PhenomenaFrench-language works237,207