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Record W2746321994 · doi:10.3847/1538-4357/aa8631

The Green Bank Ammonia Survey: Dense Cores under Pressure in Orion A

2017· article· en· W2746321994 on OpenAlexaff
Helen Kirk, Rachel Friesen, J. E. Pineda, Erik Rosolowsky, Stella S. R. Offner, Christopher D. Matzner, Philip C. Myers, James Di Francesco, P. Caselli, F. O. Alves, A. Chacón-Tanarro, Hope How-Huan Chen, Michael Chun-Yuan Chen, Jared Keown, A. Punanova, Young Min Seo, Yancy L. Shirley, Adam Ginsburg, Christine Hall, Ayushi Singh, Héctor G. Arce, Alyssa Goodman, P. G. Martin, E. Redaelli

Bibliographic record

VenueThe Astrophysical Journal · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsQueen's UniversityUniversity of VictoriaUniversity of AlbertaCanadian Institute for Theoretical AstrophysicsUniversity of TorontoHerzberg Institute of Astrophysics
Fundersnot available
KeywordsGreen Bank TelescopeVirial theoremMolecular cloudAstrophysicsThermalPhysicsVelocity dispersionHigh pressureJames Clerk Maxwell TelescopeAstronomyStar formationStarsMeteorologyMechanicsGalaxy

Abstract

fetched live from OpenAlex

Abstract We use data on gas temperature and velocity dispersion from the Green Bank Ammonia Survey and core masses and sizes from the James Clerk Maxwell Telescope Gould Belt Survey to estimate the virial states of dense cores within the Orion A molecular cloud. Surprisingly, we find that almost none of the dense cores are sufficiently massive to be bound when considering only the balance between self-gravity and the thermal and non-thermal motions present in the dense gas. Including the additional pressure binding imposed by the weight of the ambient molecular cloud material and additional smaller pressure terms, however, suggests that most of the dense cores are pressure-confined.

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 score0.062
Threshold uncertainty score0.124

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.021
GPT teacher head0.271
Teacher spread0.250 · 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

Citations82
Published2017
Admission routes1
Has abstractyes

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