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Record W2023688641 · doi:10.1063/1.1795713

Constant-volume heat capacity in a near-critical fluid from Monte Carlo simulations

2004· article· en· W2023688641 on OpenAlexafffund
Christopher D. Daub, Philip J. Camp, G. N. Patey

Bibliographic record

VenueThe Journal of Chemical Physics · 2004
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of British Columbia
FundersWestern Canada Research Grid
KeywordsMonte Carlo methodCriticalityHeat capacityUniversality (dynamical systems)Statistical physicsIsing modelCritical exponentSpecific heatPhysicsCanonical ensembleRenormalization groupThermodynamicsGrand canonical ensembleMathematicsCondensed matter physicsPhase transitionMathematical physics

Abstract

fetched live from OpenAlex

We consider a near-critical fluid of hard spheres with short-range interactions (approximately r(-6)) and obtain its constant-volume heat capacity C(V) by means of Monte Carlo calculations in the canonical ensemble. The question addressed is whether or not the heat capacities of the finite-size systems studied in simulations can provide a reliable indication of nonclassical criticality. For the model fluid considered here this is found to be the case. The heat capacity along the critical isochore shows a peak near the critical temperature, with a system size dependence that is consistent with the known Ising universality class of the model. The relevance of our results to recent attempts to determine the universality class of ionic fluids through calculations of C(V) is briefly discussed.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.017
GPT teacher head0.226
Teacher spread0.209 · 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 designSimulation or modeling
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

Citations4
Published2004
Admission routes2
Has abstractyes

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