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Record W3098442773

Class 0 Protostars in the Perseus Molecular Cloud: A Correlation Between the Youngest Protostars and the Dense Gas Distribution

2014· article· en· W3098442773 on OpenAlexaff
Sarah Sadavoy, James Di Francesco, S. Pezzuto, A. Maury, F. Motte, N. Schneider, D. Arzoumanian, S. Bontemps, D. Elia, M. Hennemann, T. Hill, F. Louvet, N. Peretto, A. L. Roy, G. J. White

Bibliographic record

VenueOpen Research Online (The Open University) · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of TorontoNational Research Council CanadaUniversity of Victoria
Fundersnot available
KeywordsProtostarMolecular cloudPhysicsCloud computingAstrophysicsDistribution (mathematics)AstronomyStar formationStarsMathematicsComputer scienceMathematical analysis
DOInot available

Abstract

fetched live from OpenAlex

We use PACS and SPIRE continuum data at 160 μm, 250 μm, 350 μm, and 500 μm from the <i>Herschel</i> Gould Belt Survey to sample seven clumps in Perseus: B1, B1-E, B5, IC 348, L1448, L1455, and NGC 1333. Additionally, we identify and characterize the embedded Class 0 protostars using detections of compact <i>Herschel</i> sources at 70 μm as well as archival <i>Spitzer</i> catalogs and SCUBA 850 μm photometric data. We identify 28 candidate Class 0 protostars, four of which are newly discovered sources not identified with Spitzer. We find that the star formation efficiency of clumps, as traced by Class 0 protostars, correlates strongly with the flatness of their respective column density distributions at high values. This correlation suggests that the fraction of high column density material in a clump reflects only its youngest protostellar population rather than its entire source population. We propose that feedback from either the formation or evolution of protostars changes the local density structure of clumps.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0020.002
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 head0.040
GPT teacher head0.321
Teacher spread0.282 · 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.

Study designTheoretical or conceptual
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

Citations62
Published2014
Admission routes1
Has abstractyes

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