Cellular automaton modelling of lightning-induced and man made forest fires
Bibliographic record
Abstract
Abstract. The impact of forest fires on nature and civilisation is conflicting: on one hand, they play an irreplaceable role in the natural regeneration process, but on the other hand, they come within the major natural hazards in many regions. Their frequency-area distributions show power-law behaviour with scaling exponents α in a quite narrow range, relating wildfire research to the theoretical framework of self-organised criticality. Examples of self-organised critical behaviour can be found in computer simulations of simple cellular automaton models. The established self-organised critical Drossel-Schwabl forest fire model is one of the most widespread models in this context. Despite its qualitative agreement with event-size statistics from nature, its applicability is still questioned. Apart from general concerns that the Drossel-Schwabl model apparently oversimplifies the complex nature of forest dynamics, it significantly overestimates the frequency of large fires. We present a modification of the model rules that distinguishes between lightning-induced and man made forest fires and enables a systematic increase of the scaling exponent α by approximately 1/3. In addition, combined simulations using both the original and the modified model rules predict a dependence of the overall event-size distribution on the ratio of lightning induced and man made fires as well as a splitting of their partial distributions. Lightning is identified as the dominant mechanism in the regime of the largest fires. The results are confirmed by the analysis of the Canadian Large Fire Database and suggest that lightning-induced and man made forest fires cannot be treated separately in wildfire modelling, hazard assessment and forest management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".