A refinement of models projecting future Canadian fire regimes using homogeneous fire regime zones
Bibliographic record
Abstract
Broad-scale fire regime modelling is frequently based on large ecological and (or) administrative units. However, these units may not capture spatial heterogeneity in fire regimes and may thus lead to spatially inaccurate estimates of future fire activity. In this study, we defined homogeneous fire regime (HFR) zones for Canada based on annual area burned (AAB) and fire occurrence (FireOcc), and we used them to model future (2011–2040, 2041–2070, and 2071–2100) fire activity using multivariate adaptive regression splines (MARS). We identified a total of 16 HFR zones explaining 47.7% of the heterogeneity in AAB and FireOcc for the 1959–1999 period. MARS models based on HFR zones projected a 3.7-fold increase in AAB and a 3.0-fold increase in FireOcc by 2100 when compared with 1961–1990, with great interzone heterogeneity. The greatest increases would occur in zones located in central and northwestern Canada. Much of the increase in AAB would result from a sharp increase in fire activity during July and August. Ecozone- and HFR-based models projected relatively similar nationwide FireOcc and AAB. However, very high spatial discrepancies were noted between zonations over extensive areas. The proposed HFR zonation should help providing more spatially accurate estimates of future ecological patterns largely driven by fire in the boreal forest such as biodiversity patterns, energy flows, and carbon storage than those obtained from large-scale multipurpose classification units.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".