A Probabilistic Cellular Automata Framework for Assessing the Impact of WUI Fires on Communities
Bibliographic record
Abstract
The ‘wildland–urban interface’ (WUI) is a term commonly used to describe areas where wildfires and the built environment have the potential to interact resulting in loss of properties and potential loss of life. Significant residential losses associated with wildland interface fires have occurred worldwide in recent years and substantial research has been conducted on developing numerical models of ignition due to convection and ember attacks. These studies provide substantial insight into the behaviour and growth of wildland fires, which have been further utilized to build fire exposure rating of structures. The FireWise program in the United States and the FireSmart manual in Canada are two key examples of provisions developed for determining fire exposure ratings for a structure. While previous studies provide significant contribution to modelling fire propagation, a much more comprehensive model is required, which would encompass all the key variables associated with WUI fires. This paper aims at extending previously conducted efforts by developing a simulation-based model. A typical fire propagation simulation requires solving the coupled fluid-thermal differential equations which results in extreme run times making it unsuitable for general purposes, however the model in this study utilizes theory of cellular automata, which reduces the processing times substantially by simplifying the underlying equations involved. Cellular automata utilize a specific set of rules to model propagation by convection as well as ember travel. In addition, the model also considers key parameters such as humidity, nature of vegetation and topology while evaluating the propagation paths. Due to the flexible nature of the model its accuracy can be tuned to a certain extent by optimizing the propagation rules using real-event data
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".