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Record W3081976656 · doi:10.3847/1538-4357/aba52f

Dissecting the Global Cold Dust Properties and Possible Submillimeter Excess of 13 Nearby Spiral Galaxies from the NGLS

2020· article· en· W3081976656 on OpenAlexaff
Zhengxue Chang, Jianjun Zhou, C. D. Wilson, Jarken Esimbek, Jianjie Qiu, Minhua Zhou, Yuxin He, Weiguang Ji, Xindi Tang, Gang Wu, Jun Li

Bibliographic record

VenueThe Astrophysical Journal · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhysicsAstrophysicsSpiral galaxyGalaxyBlack-body radiationAstronomyLuminous infrared galaxySpectral energy distributionLuminosityMetallicitySubmillimeter ArrayStar formationRadiation

Abstract

fetched live from OpenAlex

Abstract We select 13 nearby spiral galaxies from the Nearby Galaxies Legacy Survey (NGLS) project and perform spectral energy distribution fitting for each galaxy applying two-component modified blackbody models on a global scale aim to probe the potential submillimeter (submm) excess. We find that NGC 2976, NGC 3351, and NGC 4631 show excess emission at 850 μm when using β c = 2.0. The contributions of CO(3–2), free–free emission or synchrotron radiation cannot explain their 850 μm excess. Our results suggest that a submm excess at 850 μm may be more easily detected for galaxies with faint total infrared luminosity and low cold dust mass. The colder temperature of cold dust, the more radiation of dust there is at 850 μm. The submm excess are prone to be detected in spiral galaxies with low stellar mass. As the metallicity of galaxies become poor, the submm excess is more obvious.

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 score0.009
Threshold uncertainty score0.018

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 communication0.0000.000
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 head0.019
GPT teacher head0.211
Teacher spread0.191 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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