Effects of climate on occurrence and size of large fires in a northern hardwood landscape: historical trends, forecasts, and implications for climate change in Témiscamingue, Québec
Bibliographic record
Abstract
Abstract Questions:What climate variables best explain fire occurrence and area burned in the Great Lakes‐St Lawrence forest of Canada? How will climate change influence these climate variables and thereby affect the occurrence of fire and area burned in a deciduous forest landscape in Témiscamingue, Québec, Canada? Location:West central Québec and the Great Lakes‐St Lawrence forest of Canada. Methods:We first used an information‐theoretic framework to evaluate the relative role of different weather variables in explaining occurrence and area burned of large fires (>200 ha, 1959‐1999) across the Great Lakes‐St Lawrence forest region. Second, we examined how these weather variables varied historically in Témiscamingue and, third, how they may change between the present and 2100 according to different scenarios of climate change based on two Global Circulation Models. Results:Mean monthly temperature maxima during the fire season (Apr‐Oct) and weighted sequences of dry spells best explained fire occurrence and area burned. Between 1910 and 2004, mean monthly temperature maxima in Témiscamingue showed no apparent temporal trend, while dry spell sequences decreased in frequency and length. All future scenarios show an increase in mean monthly temperature maxima, and one model scenario forecasts an increase in dry spell sequences, resulting in a slight increase in forecasted annual area burned. Conclusion:Despite the forecasted increase in fire activity, effects of climate change on fire will not likely affect forest structure and composition as much as natural succession or harvesting and other disturbances, principally because of the large relative difference in area affected by these processes.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".