Decreasing average wildfire size through random fuel treatments: A boreal forest case study.
Bibliographic record
Abstract
Area burned in boreal forests is increasing due to climate change effects and regional increases in fuels due to a history of successful fire suppression. An increase in area burned threatens valuable resources and infrastructure in timber resources areas and communities. The ecological integrity of protected areas may also be threatened if fires increase in frequency and size beyond what would have occurred prior to effective fire suppression and the effects of climate change. Fuel management is one strategy being tested by fire management agencies and researchers to address these concerns. However the pattern of fuel management that best regulates area burned has yet to be determined. This thesis investigates random fragmentation of highly flammable fuels in the boreal forests of North-western Ontario. A case study of Quetico Provincial Park is used. Using the fire growth simulation model, Prometheus, I tested whether, under extreme fire behaviour conditions, fuel isolation (FI) and fuel conversion (FC) were effective at reducing average area burned in the park. Through the simulation of over 21,000 large fires, I determined that FI and FC are effective in significantly reducing area burned for this case study. Based on these findings, random FI and FC should be studied further on a regional basis and as a prescriptive, proactive method of reducing area burned in boreal forests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".