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Record W4405012127 · doi:10.3847/1538-4357/ad8b4e

The Environments of Green Pea Galaxies. II. The Hα Dot Sample

2024· article· en· W4405012127 on OpenAlexfundno aff
Brooke Kimsey-Miller, Samantha W. Brunker, John J. Salzer

Bibliographic record

VenueThe Astrophysical Journal · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryIndiana University BloomingtonOffice of ScienceMax-Planck-Institut für AstronomieUniversity of Colorado BoulderMinistério da Ciência, Tecnologia e InovaçãoUniversity of OxfordYork UniversityLeibniz-GemeinschaftUniversity of Notre DameInstituto de Astrofísica de CanariasCarnegie Mellon UniversityUniversidad Nacional Autónoma de MéxicoAlfred P. Sloan FoundationUniversity of WashingtonJohns Hopkins UniversityCarnegie Institution of WashingtonUniversity of UtahOhio State UniversityU.S. Department of EnergySmithsonian InstitutionNew Mexico State UniversityUniversity of PortsmouthVanderbilt UniversityYale UniversityMax-Planck-Institut für Astrophysik
KeywordsPhysicsGalaxyAstrophysicsAstronomySample (material)

Abstract

fetched live from OpenAlex

Abstract We use spectroscopic redshifts obtained with the WIYN 3.5 m telescope to study the local galactic environment around 18 Green Pea (GP) galaxies. Our sample of GPs were discovered in the H α Dot Survey via their [O iii ] λ 5007 emission and span the redshift range 0.32 < z < 0.34. We present the environmental analyses near the location of the H α Dot GPs. We utilize three different density estimators, chosen based on the geometry of the redshift survey data within the H α Dot GP fields. We classify each H α Dot GP into an environment category, from extremely isolated to highly clustered. We find that our entire sample of H α Dot GPs reside in extremely isolated or low-density environments, with none located in the intermediate- to high-density regimes. We interpret this to indicate that major mergers and interactions do not drive the activity seen in these galaxies.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.257
Teacher spread0.246 · 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.

Study designOther design
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

Citations4
Published2024
Admission routes1
Has abstractyes

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