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Record W2316778796 · doi:10.1088/0004-6256/149/3/116

THE RINGS SURVEY. I. H<i>α</i>AND H i VELOCITY MAPS OF GALAXY NGC 2280

2015· article· en· W2316778796 on OpenAlexaff

Bibliographic record

VenueThe Astronomical Journal · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsSpiral galaxyKinematicsGalaxyUnbarred spiral galaxyInterferometryIrregular galaxySpiral (railway)Barred spiral galaxyAperture synthesis

Abstract

fetched live from OpenAlex

Precise measurements of gas kinematics in the disk of a spiral galaxy can be used to estimate its mass distribution. The Southern African Large Telescope has a large collecting area and field of view, and is equipped with a Fabry–Pérot (FP) interferometer that can measure gas kinematics in a galaxy from the H α line. To take advantage of this capability, we have constructed a sample of 19 nearby spiral galaxies, the RSS Imaging and Spectroscopy Nearby Galaxy Survey, as targets for detailed study of their mass distributions and have collected much of the needed data. In this paper, we present velocity maps produced from H α FP interferometry and H i aperture synthesis for one of these galaxies, NGC 2280, and show that the two velocity measurements are generally in excellent agreement. Minor differences can mostly be attributed to the different spatial distributions of the excited and neutral gas in this galaxy, but we do detect some anomalous velocities in our H α velocity map of the kind that have previously been detected in other galaxies. Models produced from our two velocity maps agree well with each other and our estimates of the systemic velocity and projection angles confirm previous measurements of these quantities for NGC 2280.

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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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