Trends in wildfire burn severity across Canada, 1985 to 2015
Bibliographic record
Abstract
Burn severity is an important component of the fire regime that has not yet been fully characterized for the forests of Canada. The objectives of this study were to (i) create a Canada-wide geospatial database of burn severity for wildland fires across forested regions of Canada from 1985 to 2015, and (ii) use this database to evaluate seasonal and annual trends in burn severity across Canada and regionally using two different regional units (ecozones and Homogeneous Fire Regime zones). We developed the 30 m resolution geospatial Canadian Landsat Burn Severity (CanLaBS) product from Landsat imagery, using values of pre-fire to post-fire differences in normalized burn ratios (dNBRs) for nearly 60 Mha of area burned across Canada’s forests from 1985 to 2015, complemented with estimates of pre-fire forest composition, crown closure, and biomass. Our results suggest that burn severity is generally lower in spring fires than in summer ones nationally and in almost every regional unit. We found no consistent relationship between burn severity and annual area burned across ecozones. Finally, we observed a small but significant decrease in burn severity from 1985 to 2015 across Canada, although this is regionally variable. The CanLaBS database is publicly available at https://doi.org/10.23687/b1f61b7e-4ba6-4244-bc79-c1174f2f92cd .
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".