Assessing the potential of the differenced Normalized Burn Ratio (dNBR) for estimating burn severity in eastern Canadian boreal forests
Bibliographic record
Abstract
There is considerable variation in the degree of burn severity in boreal fires. One approach that has been used to capture this variation from field and remote sensing perspectives for western Canadian boreal forests is the Composite Burn Index (CBI) and differenced Normalized Burn Ratio (dNBR). Of interest was how well these methods may perform for fires in eastern Canada. This study investigated the CBI-dNBR relationship for selected fires in the eastern boreal forests of Canada, with a view towards contributing to the generalisation of a Canada-wide model. Results for the sampled region showed no difference in the CBI-dNBR relationship between black spruce- and jack pine-dominated stands, whereas this relationship was best described by a Generalised Additive Model (GAM). The dNBR-derived maps would also be useful in support of research and post-fire management in burns outside the studied territory and time frame covered by the existing burn severity mapping system already used in this region. The Saturated growth model proposed for the western boreal region also performed well for our eastern boreal region, thus further supporting the development of a national model.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".