Propagation limits and velocity of reaction-diffusion fronts in a system of discrete random sources
Bibliographic record
Abstract
The effect of spatially randomizing a system of pointlike sources on the propagation of reaction-diffusion fronts is investigated in multidimensions. The dynamics of the reactive front are modeled by superimposing the solutions for diffusion from a single point source. A nondimensional parameter is introduced to quantify the discreteness of the system, based on the characteristic reaction time of sources compared to the diffusion time between sources. The limits to propagation and the average velocity of propagation are expressed as probabilistic quantities to account for the influence of the randomly distributed sources. In random systems, two- and three-dimensional fronts are able to propagate beyond a limit previously found for systems with regularly distributed sources, while a propagation limit in one dimension that is independent of domain size cannot be defined. The dimensionality of the system is seen to have a strong influence on the front propagation velocity, with higher dimensional systems propagating faster than lower dimensional systems. In a three-dimensional system, both the limit to propagation and average front velocity revert to a solution that assumes a spatially continuous source function as the discreteness parameter is increased to the continuum limit. The results indicate that reactive systems are able to exploit local fluctuations in source concentration to extend propagation limits and increase the velocity in comparison to regularly spaced systems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".