Front propagation in heterogeneous media: mathematical, numerical, and statistical issues in modelling a forest fire front Chris Bose (University of Victoria),
Bibliographic record
Abstract
The main objective of this workshop was to bring together applied mathematicians, statisticians, and forest fire researchers and managers to discuss key issues relating to numerical algorithms, physical modelling and mathematical/statistical analysis relevant to the simulation of a propagating forest fire front. Numerical Algorithms The most popular wildfire spread simulators used in Canada and the United States are PROMETHEUS (Tymstra, 2005) and FARSITE (Finney, 2004). Both of these simulators are based on a marker method solution of a Lagrangian form of partial differential equations which describe the evolving fire front (for example, Richards, 1990). The principal difference between the two simulators lies in the manner in which the input parameters are determined. Inputs are derived from fuel type (i.e. type, density and characteristics of vegetation), weather (wind speed and direction, relative humidity, temperature, and precipitation), and topography (i.e. elevation, slope and aspect). In Canada, an empirical modelling approach (based on observations on a large inventory of experimental and well-documented wildfires) has been employed in order to relate the partial differential equation parameters to these inputs, while in the United States, a physical process approach (based on extensive laboratory experimentation) has been employed. Remarkably (or perhaps because of the robustness of the equation solutions), the two approaches often yield similar results.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".