Detecting regional differences in within-wildfire burn patterns in western boreal Canada
Bibliographic record
Abstract
Under the auspices of ecosystem-based management (EBM), using historical range of variation (HRV) knowledge to help guide forest management decision-making is becoming commonplace. In support of this evolution, we hypothesized that historical fire-scale wildfire burn patterns in western boreal Canada could be differentiated by major ecological zones. We tested 10 fine-scale burn pattern metrics for 129 natural wildfires across more than 100 million ha of western boreal Canada against existing Canadian and provincial ecological classification schemes. The results showed some evidence of two historic disturbance regimes. Fires in the Foothills and Rocky Mountain ecoregions tended to have more disturbed patches, a smaller dominant disturbed patch, and less area in partially disturbed island remnants relative to fires in the Boreal Forest and Boreal Shield. However, several key metrics such as event shape and total remnant area were zone-invariant. Fire regime parameters such as fire size and frequency may not be linked to more detailed fire behaviour parameters such as remnant patterns. The moderate, yet highly variable levels of remnant pattern variation we found across the study area represents a natural, and potentially universal source of structural and compositional diversity for the boreal that may be critical to its sustainability.
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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) | 0.000 | 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 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".