Fire and the relative roles of weather, climate and landscape characteristics in the Great Lakes‐St. Lawrence forest of Canada
Bibliographic record
Abstract
Abstract Question: In deciduous‐dominated forest landscapes, what are the relative roles of fire weather, climate, human and biophysical landscape characteristics for explaining variation in large fire occurrence and area burned? Location: The Great Lakes‐St. Lawrence forest of Canada. Methods: We characterized the recent (1959–1999) regime of large (≥ 200 ha) fires in 26 deciduous‐dominated landscapes and analysed these data in an information‐theoretic framework to compare six hypotheses that related fire occurrence and area burned to fire weather severity, climate normals, population and road densities, and enduring landscape characteristics such as surficial deposits and large lakes. Results: 392 large fires burned 833 698 ha during the study period, annually burning on average 0.07%± 0.42% of forested area in each landscape. Fire activity was strongly seasonal, with most fires and area burned occurring in May and June. A combination of antecedent‐winter precipitation, fire season precipitation deficit/surplus and percent of landscape covered by well‐drained surficial deposits best explained fire occurrence and area burned. Fire occurrence varied only as a function of fire weather and climate variables, whereas area burned was also explained by percent cover of aspen and pine stands, human population density and two enduring characteristics: percent cover of large water bodies and glaciofluvial deposits. Conclusion: Understanding the relative role of these variables may help design adaptation strategies for forecasted increases in fire weather severity by allowing (1) prioritization of landscapes according to enduring characteristics and (2) management of their composition so that substantially increased fire activity would be necessary to transform landscape structure and composition.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".