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Record W2276306513 · doi:10.1017/s1743921316008000

Bridging Galaxy Dynamics and Baryon Efficiency of 40 EDGE-CALIFA galaxies

2015· article· en· W2276306513 on OpenAlexaff
V. Kalinova, Dario Colombo, Erik Rosolowsky

Bibliographic record

VenueProceedings of the International Astronomical Union · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhysicsAstrophysicsGalaxyHaloBaryonStellar massDark matter haloDark matterGalaxy rotation curveStar formation

Abstract

fetched live from OpenAlex

Abstract We apply the Jeans Axisymmetric Multi-Gaussian Expansion method to the stellar kinematic maps of 40 Sa–Sd EDGE-CALIFA galaxies and derive their circular velocity curves (CVCs). The CVCs are classified using the Dynamical Classification method developed in Kalinova et al. (2015). We also calculate the observational baryon efficiency, OBE, where M*/Mb=M*/(M*+MHI+MH2) of the galaxies using their stellar mass, total neutral hydrogen mass and total molecular gas from CO luminosities. Slow-rising, Flat and Round-peaked CVC types correspond to specific OBEs, stellar and dark matter (DM) halo mass values, while the Sharp-peaked CVCs span in the whole DM halo mass range of 1011-1014M⊙.

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)0.001
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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.008
GPT teacher head0.203
Teacher spread0.196 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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