Stochastic simulations for aggregating systems with non-constant reaction rates
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Bibliographic record
Abstract
<p> In this thesis, reactive multi-particle collision dynamics (RMPC) is used for the simulation of aggregation and aggregation-fragmentation systems. RMPC dynamics consists of collisions, reactions, and free streaming. Aggregation and fragmentation is modelled using a reactive mechanism. An infinite system of ODEs called the Smoluchowski differential equations has been used for comparison in the well mixed case. The exact solution for the infinite system is also compared with a finite system RK4 solution that is more appropriate for finite system RMPC simulations. The maximum cluster size is taken to be five, and the domain for stochastic simulations is cubic with periodic boundary conditions. Constant, additive, and multiplicative rates are discussed, and the affects of variations in aggregation and break-up rates are observed. Non-zero, monomer-only initial conditions are used, and the solution for aggregation is obtained with a monomer only initial-concentration equal to 1, as well as b, where b is a constant. The solution for aggregation and break-up is calculated using a monomer-only initial concentration equal to b. The RMPC simulations showed that the RMPC results had a good agreement with the finite-system RK4 solution specially for smaller particle sizes. There was stochastic noise in the RMPC results for all cases that became more pronounced with increase in break-up rate. The novelty of this work consists of RMPC simulation results for additive and multiplicative rates, which has not been simulated using RMPC before. For the system size considered in this work, stochastic effects can be further extended for larger cluster sizes, and to analyse different choices for aggregation and break-up rates. </p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it