An alternative fire regime zonation for Canada
Bibliographic record
Abstract
The ability of national and multipurpose ecological classification systems to provide an optimal zonation for a fire regime is questionable. Using wildfire (>1 ha) point data for the 1980–99 period, we defined zones with a homogeneous fire regime (HFR) across Canada and we assessed how these differ from the National Ecological Framework for Canada (NEFC) units of corresponding scale, i.e. ecoprovinces. Two HFR zonations were produced through spatially constrained clustering of (i) 1600-km2 cells and (ii) the smallest units of the NEFC system, i.e. ecodistricts, using attributes for natural and anthropogenic fires. Thirty-three HFR zones were identified. HFR zonations showed smaller differences among each other than with NEFC ecoprovinces. Comparisons with ecoprovinces suggested general agreement of generalised fire regime values with HFR zones but with poor zone boundary correspondence. Ecoprovince zonation led to an overgeneralisation of fire regime estimates with less variation captured than by the HFR zonations, especially that using gridded fixed-area cells. Estimates of fire-return interval strongly differed between a priori and HFR zonations. The use of large-scale NEFC units or a zonation using its smallest units may constrain our ability to accurately quantify and portray fire regime variability across the country. The alternative empirical HFR zonation using gridded cells refines the location and nature of fire risk.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".