Stellar accretion and associated processes: Perspectives from kinetic theory and thermodynamics
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".