Analytic expressions for correlations in coarse-grained simple fluids
Bibliographic record
Abstract
Coarse-graining of fluids is challenging because fluid particles are unbound and diffuse long distances in time. One approach creates coarse-grain variables that group all particles within a region centered on specific points in space and accounts for the movement of particles among such regions. In our previous work, we showed that in many cases, potential interactions for such a scheme adopted a generalized quadratic form, whose parameters depend on means, variances, and correlation coefficients among the coarse-grain variables. In this work, we use statistical mechanics to derive analytic expressions for these parameters, using properties of the fluid, including pair distribution functions. These expressions are compared against simulation-derived values and shown to be in good agreement. This approach can be used to calculate a priori the potential for any homogeneous, simple fluid, without the need for fitting procedures or matching, thus increasing the ease of use of this coarse-grain scheme and creating a foundation for large-scale bottom-up simulations. Furthermore, these expressions provide a quantitative way of studying the boundary between discrete (atomic) and continuum models of fluids.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".