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Record W2133065879 · doi:10.1088/0004-637x/767/2/126

THE HERSCHEL AND JCMT GOULD BELT SURVEYS: CONSTRAINING DUST PROPERTIES IN THE PERSEUS B1 CLUMP WITH PACS, SPIRE, AND SCUBA-2

2013· article· en· W2133065879 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of TorontoNational Research Council CanadaUniversity of Victoria
FundersScience and Technology Facilities Council
KeywordsJames Clerk Maxwell TelescopeProtein filamentWavelengthStar formationData reductionProtostarInfrared

Abstract

fetched live from OpenAlex

We present Herschel observations from the Herschel Gould Belt Survey and SCUBA-2 science verification observations from the JCMT Gould Belt Survey of the B1 clump in the Perseus molecular cloud. We determined the dust emissivity index using four different techniques to combine the Herschel PACS+SPIRE data at 160-500 μm with the SCUBA-2 data at 450 μm and 850 μm. Of our four techniques, we found that the most robust method was filtering out the large-scale emission in the Herschel bands to match the spatial scales recovered by the SCUBA-2 reduction pipeline. Using this method, we find β 2 toward the filament region and moderately dense material and lower β values (β ≳ 1.6) toward the dense protostellar cores, possibly due to dust grain growth. We find that β and temperature are more robust with the inclusion of the SCUBA-2 data, improving estimates from Herschel data alone by factors of 2 for β and by 40% for temperature. Furthermore, we find core mass differences of ≲ 30% compared to Herschel-only estimates with an adopted β = 2, highlighting the necessity of long-wavelength submillimeter data for deriving accurate masses of prestellar and protostellar cores. © 2013. The American Astronomical Society. All rights reserved.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.953

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.219
Teacher spread0.201 · 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

Citations134
Published2013
Admission routes1
Has abstractyes

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