The Effects of Fuel Type Map Accuracy on Fire Behaviour Metrics
Bibliographic record
Abstract
For fire managers and industry professionals, monitoring and leading wildfire prevention efforts as well as reactionary efforts require accurate and operable fuel type maps to achieve effective management. Fuel type map classification accuracy has been seen to have varying values across industries and applications (> 10 %), and the consequences of these misclassifications in fuel type mapping has yet to be determined. The objective of this research was to explore the effects of mapping error on fire behavior metrics, burn probability, fire intensity, and rate of spread in the southern interior forest region of British Columbia which experiences dry weather and extreme fire conditions. Utilizing a fuel type map with 250 m resolution produced through an artificial neural network as a base case that has assumed 100% accuracy; induced error at varying levels of intensity (10%, 20%, 30%) was applied by selecting C-7 (conifer plantation/Ponderosa pine-Douglas-fir) pixels, and reassigning them to fuel types C-2 (Boreal Spruce) and M-1/2 (Boreal mixedwood) which have been commonly misrepresented in classification. With three levels of error and a base case for comparison, simulations were conducted through a spatial fire simulation software, Burn-P3, to determine effects. Clear trends were found to show that there was not a noticeable change in fire behavior metrics between the base case and 10 % error but that a relative inflection point was found between 10 percent and 20 percent. It was found that fire behavior metrics increased in intensity and spatial reach when fuel type mapping error increased. Recommendations for future research such as a complete evaluation of all error classes between 0% and 100%, as well as the implementation of map accuracy assessments are given to aid wildfire management efforts.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".