Variability and drivers of burn severity in the northwestern Canadian boreal forest
Bibliographic record
Abstract
Abstract Burn severity (ecological impacts of fire on vegetation and soils) influences post‐fire stand structure and species composition. The spatial pattern of burn severity may compound the ecological impacts of fire through distances to seed sources and availability of bud banks and seedbeds. Land managers require spatial burn severity data to manage post‐fire risks, ecosystem recovery, and assess the outcomes of fires. This research seeks to characterize and explain variability in burn severity in the northwestern boreal forest. We assessed burn severity one year post‐fire in six large wildfires that burned in 2014. We measured burn severity using the Composite Burn Index, surface Burn Severity Index, Canopy Fire Severity Index, and percent overstory mortality, describing a range of surface and overstory fire effects. Burn severity was variable, ranging from unburned residuals to complete overstory mortality and intense combustion. We related field measurements to remotely sensed multispectral burn severity metrics of the differenced Normalized Burn Ratio (dNBR), the Relativized dNBR, and the Relativized Burn Ratio. Diagnostic models of burn severity using relativized metrics had lower errors and better (though not significantly so) fits to the field data. Spatial patterns of burn severity were consistent with those observed in other large fires in North America. Stand‐replacing patches were large, aggregated, and covered the largest proportion of the landscape. These patterns were not consistent across the four mapped burn severity field metrics, suggesting such metrics may be viewed as related, but complementary, as they depict different aspects of severity. Prognostic models indicated burn severity was explained by pre‐fire stand structure and composition, topoedaphic context, and fire weather at time of burning. Wetlands burned less severely than uplands, and open stands with high basal areas experienced lower burn severity in upland vegetation communities. This research offers an enhanced understanding of the relationship between ground observations and remotely sensed severity metrics, in conjunction with stand‐level drivers of burn severity. The diverse fuel complexes and extreme fire weather during the 2014 fire season produced the complex patterns and broad range of burn severity observed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".