Burning Issues with Prometheus- the Canadian Wildland Fire Growth Simulation Model
Bibliographic record
Abstract
Abstract: The software package Prometheus is Canada’s only operational wildfire growth modelling simulator. The program calculates accurate, fast, multi-day forecasts of moving fire fronts and is currently used in fire fighting situations, in fire risk analysis and in the design of “fire-safe ” communities and forests. At the core of Prometheus is an algorithm that calculates the evolution of the fire front in an empirical manner, assuming locally elliptic fire spread and Huygens ’ principle in a Lagrangian description of the evolution. This report describes progress made on a set of problems that Prometheus developers brought to the 10th Pacific Institute for the Mathematical Sciences (PIMS) Industrial Problem Solving Workshop held in June 2006 at Simon Fraser University. In particular, we investigate the mathematical background behind the Prometheus code and suggest a new method (the “outer hull ” approach) to remove tangles from the evolving front. Other methods that can reduce tangles and crossings are “smoothing ” of data and parameters, and redistribution of vertices on the evolving front. We investigate the use of de Boor’s Algorithm as an automated procedure for redistributing vertices along the front. Finally, we investigate the level set method as a new tool for forest fire spread simulations and show in some simple test cases the excellent potential of this method. 1
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".