Wildfires in boreal ecoregions : Evaluating the power law assumption and intra-annual and interannual variations
Bibliographic record
Abstract
Wildfires are a major driver of ecosystem development and contributor to carbon emissions in boreal forests. We analyzed the contribution of fires of different fire size classes to the total burned area and suggest a novel fire characteristic, the characteristic fire size, i.e., the fire size class with the highest contribution to the burned area, its relation to bioclimatic conditions, and intra-annual and interannual variation. We used the Canadian National Fire Database (using data from 1960 to 2010) and a novel satellite-based burned area data set (2001 to 2011). We found that the fire size distribution is best explained by a normal distribution in log space in contrast to the power law-based linear fire area relationship which has prevailed in the literature so far. We attribute the difference to previous studies in the scale invariance mainly to the large extent of the investigated ecoregion as well as to unequal binning or limiting the range at which the relationship is analyzed; in this way we also question the generality of the scale invariance for ecoregions even outside the boreal domain. The characteristic fire sizes and the burned area show a weak correlation, indicating different mechanisms behind each feature. Fire sizes are found to depend markedly on the ecoregion and have increased over the last five decades for Canada in total, being most pronounced in the early season. In the late season fire size and area decreased, indicating an earlier start of the fire season.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".