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Record W1979773910 · doi:10.1086/523697

Cooling, Gravity, and Geometry: Flow‐driven Massive Core Formation

2008· article· en· W1979773910 on OpenAlex
Fabian Heitsch, Lee Hartmann, Adrianne Slyz, Julien Devriendt, Andreas Burkert

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Astrophysical Journal · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsCanadian Nautical Research Society
Fundersnot available
KeywordsPhysicsCore (optical fiber)Mass distributionGravitationGravity currentThermalGravitational collapseFlow (mathematics)MechanicsMolecular cloudClassical mechanicsAstrophysicsMeteorologyGalaxy

Abstract

fetched live from OpenAlex

<p>We study numerically the formation of molecular clouds in large-scale colliding flows including self-gravity. The models emphasize the competition between the effects of gravity on global and local scales in an isolated cloud. Global gravity builds up large-scale filaments, while local gravity, triggered by a combination of strong thermal and dynamical instabilities, causes cores to form. The dynamical instabilities give rise to a local focusing of the colliding flows, facilitating the rapid formation of massive protostellar cores of a few hundred <i>M</i><sub>☉</sub>. The forming clouds do not reach an equilibrium state, although the motions within the clouds appear to be comparable to virial. The self-similar core mass distributions derived from models with and without self-gravity indicate that the core mass distribution is set very early on during the cloud formation process, predominantly by a combination of thermal and dynamical instabilities rather than by self-gravity.</p>

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.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.243
Teacher spread0.225 · 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