Characterization of wildfire regimes in Canadian boreal terrestrial ecosystems
Bibliographic record
Abstract
Wildfire is a major disturbance in boreal terrestrial ecosystems. Characterizing fire regimes and projecting fire recurrence intervals for different biomes are important in managing those ecosystems and quantifying carbon dynamics of those ecosystems. This study used Canadian wildfire datasets, 1980–1999, to characterize relationships between number of fires and burned area for 13 ecozones and to calculate wildfire recurrence intervals in each ecozone. For the study period, wildfires were found to follow power–law relationships between frequency densities (number of fires normalized to unit bins) and burned areas in all ecozones. Power–law frequency–area relationships also held for both anthropogenic fires and natural fires in the 1980s and 1990s. For each Canadian ecozone using the parameters of the power–law frequency–area distributions, fire recurrence intervals were then calculated for wildfires equal to or larger than a given size of burned area. Fire recurrence intervals ranged from 1 to 32 years for burned areas >2 km2, and from 1 to 100 years for burned areas >10 km2 in every 10 000-km2 spatial area for each ecozone. The information obtained through characterizing the wildfires and the fire recurrence intervals calculated in this study will provide guidance to wildfire risk managers throughout Canada. The findings of this study will also be a benefit to future efforts in quantifying carbon dynamics in Canadian boreal terrestrial ecosystems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.001 | 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".