A cluster growth model for heterogeneous nucleation
Bibliographic record
Abstract
Abstract In previous works, we showed that the Keller–Rubinow model of Liesegang ring formation could be regularized by hypothesizing a bistable transition associated with the heterogeneous nucleation of the dichromate on impurities within the gel of the experiment. This hypothesis eliminated an ill-posedness associated with this model, wherein the ‘rings’ which formed were of infinitesimal thickness, owing to a discontinuity in the model formulation. In the present paper, we consider a discrete stochastic model for nucleation, and show that it can provide a basis for our earlier hypothesis. The result relies on the idea that the detachment rate of the dichromate ions from the impurity surface depends on already present clusters, through a thermodynamic unmixing coefficient. We provide a Monte Carlo simulation of the process, and we derive a stochastic model of it as a sequence of differential equations for the saturation probabilities. Solution of the model gives results which are in agreement with the results of the simulations, and also with a much simpler mean field approximation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".