The influence of weather and fuel type on the fuel composition of the area burned by forest fires in Ontario, 1996–2006
Bibliographic record
Abstract
Forest fires are influenced by weather, fuels, and topography, but the relative influence of these factors may vary in different forest types. Compositional analysis can be used to assess the relative importance of fuels and weather in the boreal forest. Do forest or wild land fires burn more flammable fuels preferentially or, because most large fires burn in extreme weather conditions, do fires burn fuels in the proportions they are available despite differences in flammability? In the Canadian boreal forest, aspen (Populus tremuloides) has been found to burn in less than the proportion in which it is available. We used the province of Ontario's Provincial Fuels Database and fire records provided by the Ontario Ministry of Natural Resources to compare the fuel composition of area burned by 594 large (>40 ha) fires that occurred in Ontario's boreal forest region, a study area some 430,000 km2 in size, between 1996 and 2006 with the fuel composition of the neighborhoods around the fires. We found that, over the range of fire weather conditions in which large fires burned and in a study area with 8% aspen, fires burn fuels in the proportions that they are available, results which are consistent with the dominance of weather in controlling large fires.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".