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Record W7084597635 · doi:10.4006/0836-1398-38.3.177

Stellar accretion and associated processes: Perspectives from kinetic theory and thermodynamics

2025· article· en· W7084597635 on OpenAlexvenueno aff

Bibliographic record

VenuePhysics Essays · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsProtostarGravitational collapseLaws of thermodynamicsGravitationZeroth law of thermodynamicsIdeal gasExtended irreversible thermodynamicsKinetic theoryAccretion (finance)Thermal equilibrium

Abstract

fetched live from OpenAlex

Temperature and the laws of thermodynamics are central to physics. They serve to guide all theory that involves thermodynamic relations. Temperature, irrespective of global or local equilibrium conditions, must always be intensive to satisfy the zeroth and second laws of thermodynamics. At the same time, if the laws of thermodynamics are to be followed, not only must the units balance on each side of a thermodynamic equation but so too must thermodynamic character. The theory of protostar formation by gravitational collapse is constructed from the kinetic theory of an ideal gas. In this instance, temperature is introduced in combination with gravitation via the virial theorem. Such an approach assumes that an uncontained cloud of gas in interstellar space will gravitationally collapse, or self-compress, when sufficiently massive. Yet, experiments demonstrate that uncontained gases, irrespective of bulk mass, always expand into their surroundings. The critical mass for initiation of self-compression of a gas is the Jeans mass, which depends on the gas temperature. Similarly, stellar accretion and accretion disk relations involve temperature. All these expressions assign temperature a nonintensive character, in violation of the laws of thermodynamics. Consequently, the relations and the theories from which they are derived are invalid.

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)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.533
Threshold uncertainty score0.410

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 studies0.0000.000
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 head0.012
GPT teacher head0.225
Teacher spread0.213 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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