Using cellular automata and multi-criteria evaluation to simulate the wildfire expansion in Prince George, British Co-lumbia
Bibliographic record
Abstract
Wildfires pose a critical and ongoing challenge in British Columbia, Canada, threatening human life, property, and natural ecosystems. To understand and predict the behavior of such fires, our study employs Cellular Automata (CA), a mathematical model adept at simulating complex systems through grid-based cell interactions. This model, validated by prior research, incorporates a wind propagation rule that significantly enhances the prediction of wildfire spread in the direction of prevailing winds. Research centers on a wildfire event in Prince George, utilizing CA to simulate fire dynamics influenced by var-ious factors. The model’s strength lies in its ability to represent detailed local interactions and its flexibility in scenario testing, which is instrumental in understanding model uncer-tainties. By simulating different fire scenarios, the study aims to grasp the complexities and potential variables affecting wildfire behavior. The research provides a foundation for decision-makers to analyze and study wildfire events, leveraging a Multi-Criteria Evaluation (MCE) Model to assess the susceptibility of cells to fire. This comprehensive approach combines CA with MCE, offering a robust framework for simulating and manag-ing wildfire expansion in British Columbia.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".