A compound Poisson model for the annual area burned by forest fires in the province of Ontario
Bibliographic record
Abstract
Abstract We use the compound Poisson probability distribution to model the annual area burned by forest fires in the Canadian province of Ontario. Models for sums‐of‐random variables, relevant for modeling aggregate insurance claims and assessing insurance risk are also relevant in modeling aggregate area burned based on sums of sizes of individual fires. Researchers have fit the distribution of fire sizes to the truncated power‐law (or Pareto) distribution (Wardet al., 2001) and a four‐parameter Weibull distribution (Reed and McKelvey, 2002 ). Armstrong ( 1999 ) fitted a lognormal distribution to annual proportion of area burned by forest fires in a region of Alberta. We derive expressions and moments for aggregate area burned in Ontario using fire data from the Ontario Ministry of Natural Resources (OMNR). We derive expressions for the distribution of area burned for “severe” and “mild” fire weather scenarios and for “intensive suppression” and “no suppression” scenarios (represented by the intensive and extensive fire protection zones of the province). These distributions can be used to perform risk analysis of annual area burned. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".