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Record W1981733599 · doi:10.1088/0004-6256/148/6/127

THE INTERSTELLAR MEDIUM AND STAR FORMATION IN EDGE-ON GALAXIES. II. NGC 4157, 4565, AND 5907

2014· article· en· W1981733599 on OpenAlexaff
Kijeong Yim, Tony Wong, Rui Xue, Richard J. Rand, Erik Rosolowsky, J. M. van der Hulst, Robert A. Benjamin, E. J. Murphy

Bibliographic record

VenueThe Astronomical Journal · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Alberta
FundersCalifornia Institute of TechnologyGordon and Betty Moore FoundationJames S. McDonnell FoundationNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsPhysicsAstrophysicsRADIUSGalaxyInterstellar mediumStarsStar formationStar (game theory)

Abstract

fetched live from OpenAlex

We present a study of the vertical structure of the gaseous and stellar disks in a sample of edge-on galaxies (NGC
\n4157, 4565, and 5907) using BIMA/CARMA ^(12)CO J = 1 → 0, VLA HI, and Spitzer 3.6 μm data. In order to take
\ninto account projection effects when we measure the disk thickness as a function of radius, we first obtain the
\ninclination by modeling the radio data. Using the measurement of the disk thicknesses and the derived radial
\nprofiles of gas and stars, we estimate the corresponding volume densities and vertical velocity dispersions. Both
\nstellar and gas disks have smoothly varying scale heights and velocity dispersions, contrary to assumptions of
\nprevious studies. Using the velocity dispersions, we find that the gravitational instability parameter Q follows a
\nfairly uniform profile with radius and is ⩾1 across the star-forming disk. The star formation law has a slope that is significantly different from those found in more face-on galaxy studies, both in deprojected and pixel-by-pixel
\nplots. Midplane gas pressure based on the varying scale heights and velocity dispersions appears to roughly hold a
\npower-law correlation with the midplane volume density ratio.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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.

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

Citations72
Published2014
Admission routes1
Has abstractyes

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