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Record W4301697339 · doi:10.1051/0004-6361/201322954

Optical imaging for the<i>Spitzer</i>Survey of Stellar Structure in Galaxies

2014· article· en· W4301697339 on OpenAlexfundno aff
J. H. Knapen, Santiago Erroz-Ferrer, Javier Roa, Judit Bakos, Mauricio Cisternas, Ryan Leaman, Nik Szymanek

Bibliographic record

VenueAstronomy and Astrophysics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryBrookhaven National LaboratoryScience and Technology Facilities CouncilNational Science FoundationLiverpool John Moores UniversityYork UniversitySpace Telescope Science InstituteInstituto de Astrofísica de CanariasCarnegie Mellon UniversityUniversity of ArizonaOffice of ScienceCollege of Engineering, Michigan State UniversityPrinceton UniversityAlfred P. Sloan FoundationUniversity of WashingtonJohns Hopkins UniversityHarvard UniversityOhio State UniversityNew Mexico State UniversityUniversity of PortsmouthYale UniversityVanderbilt UniversityEuropean CommissionU.S. Department of EnergyCalifornia Institute of TechnologyNational Aeronautics and Space AdministrationJet Propulsion Laboratory
KeywordsPhysicsAstrophysicsGalaxyContext (archaeology)Luminous infrared galaxyAstronomyGeography

Abstract

fetched live from OpenAlex

Context. The Spitzer Survey for Stellar Structure in Galaxies (S4G) and its more recently approved extension will lead to a set of 3.6 and 4.5 μm images for 2829 galaxies, which can be used to study many different aspects of the structure and evolution of local 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 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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

Citations33
Published2014
Admission routes1
Has abstractyes

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