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Record W2589046651 · doi:10.1051/0004-6361/201628600

Investigating merging galaxies by using Pan-STARRS images

2017· article· en· W2589046651 on OpenAlexfundaboutno aff
Yi-Fan Lin, Po‐Chieh Yu, Jen-Chao Huang, Chorng‐Yuan Hwang, Wen-Ping Chen, Nick Kaiser, N. Metcalfe, C. Waters

Bibliographic record

VenueAstronomy and Astrophysics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryBrookhaven National LaboratorySmithsonian Astrophysical ObservatoryJet Propulsion LaboratoryNational Central UniversityOffice of ScienceMax-Planck-Institut für AstronomieUniversity of EdinburghMinistry of Science and Technology, TaiwanDurham UniversityYork UniversityCarnegie Mellon UniversityUniversity of ArizonaCollege of Engineering, Michigan State UniversityUniversity of WashingtonPrinceton UniversityAlfred P. Sloan FoundationJohns Hopkins UniversityHarvard UniversityQueen's UniversityOhio State UniversitySmithsonian InstitutionU.S. Department of EnergyCalifornia Institute of TechnologyNational Aeronautics and Space AdministrationNew Mexico State UniversityUniversity of California, Los AngelesUniversity of PortsmouthVanderbilt UniversityYale UniversityNational Science Foundation
KeywordsPhysicsTelescopeGalaxyAstrophysicsAstronomyCluster (spacecraft)Galaxy cluster

Abstract

fetched live from OpenAlex

Aims. We studied the r′-, z′-, and y′-band images of merging galaxies from the observations of the Panoramic Survey Telescope & Rapid Response System (Pan-STARRS). The merging galaxies were selected from our merging catalog that was created by checking the images of the Red-Sequence Cluster Survey 2 from the observations of the Canada France Hawaii Telescope.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.222
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2017
Admission routes2
Has abstractyes

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