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Record W2691304632 · doi:10.3847/1538-3881/152/6/225

HIGHMASS—HIGH H i MASS, H i-RICH GALAXIES AT Z ∼ 0: COMBINED H i AND H<sub>2</sub> OBSERVATIONS

2016· article· en· W2691304632 on OpenAlexaff
Gregory Hallenbeck, Shan Huang, Kristine Spekkens, Martha P. Haynes, Riccardo Giovanelli, Elizabeth A. K. Adams, J. Brinchmann, John M. Carpenter, Jayaram N. Chengalur, L. K. Hunt, Karen L. Masters, A. Saintonge

Bibliographic record

VenueThe Astronomical Journal · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsRoyal Military College of Canada
FundersJet Propulsion LaboratoryUniversities Space Research AssociationNational Radio Astronomy ObservatoryNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyGordon and Betty Moore FoundationJames S. McDonnell FoundationNational Science Foundation
KeywordsPhysicsAstrophysicsStar formationGalaxyStarsStellar massSurface brightnessDark matterAstronomy

Abstract

fetched live from OpenAlex

ABSTRACT We present resolved and CO observations of three galaxies from the HIghMass sample, a sample of -massive ( ), gas-rich ( in the top 5% for their M *) galaxies identified in the ALFALFA survey. Despite their high gas fractions, these are not low-surface-brightness galaxies and have typical specific star formation rates (SFR ) for their stellar masses. The three galaxies have normal SFRs for their masses, but unusually short star formation efficiency scale lengths, indicating that the star formation bottleneck in these galaxies is in the conversion of to , not in converting to stars. In addition, their dark matter spin parameters (λ) are above average, but not exceptionally high, suggesting that their star formation has been suppressed over cosmic time but is now becoming active, in agreement with prior Hα observations.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.010
GPT teacher head0.188
Teacher spread0.178 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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