The constant-volume heat capacity of near-critical fluids with long-range interactions: A discussion of different Monte Carlo estimates
Bibliographic record
Abstract
The constant-volume heat capacities, CV, of various near-critical fluids with long-range potentials have been obtained using both canonical and grand-canonical Monte Carlo (GCMC) calculations. In the case of the restricted primitive model it is shown that the large discrepancies between previously reported results arise from the use of different simulation ensembles. In order to investigate how well the different ensemble estimates of CV obtained with small systems can indicate the universality class of the bulk fluid, calculations have been performed for fluids with attractive pair interactions which vary like −1/ra, with a=6, 4, and 3.1. For a=6, Ising-type criticality is expected, while for a=4 and 3.1 the criticality is mean-field. For each of these models the canonical-ensemble estimates of CV do not provide unambiguous confirmation of the expected critical behavior, and hence this is not a reliable method for determining the universality class. This is also true of the GCMC estimates of CV, which appear consistent with Ising-type behavior for all of the systems studied, even for those which are known to exhibit mean-field criticality in the thermodynamic limit. We suggest that these are artifacts associated with finite system size, and we speculate as to why they appear in canonical and GCMC calculations.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".