Developing a two-level fire regime zonation system for Canada
Bibliographic record
Abstract
Fire regime zonation systems are critical tools for research and management activities. In this study, we develop a hierarchical framework that applies both qualitative and quantitative approaches to create a two-level fire regime zonation system for Canada. The finer scale level, Fire Regime Units (FRUs), was created through a stepwise synthesis of fire regime metrics based on 1970–2016 fire records, environmental attributes such as topographic features and vegetation, literature review, and expert advice. Each of these 60 FRUs exhibits an internal homogeneity in fire regime. As non-contiguous units can show similar patterns in fire-related measurements, we performed a clustering analysis on the FRUs to define 15 broad-scale Fire Regime Types (FRTs). Each type is characterized by a unique set of indices related to fire activity, seasonality, and ignition cause. This two-level fire regime zonation system has a large range of applications (e.g., modeling, gradient analysis) and is flexible enough to be updated with new data or when notable shifts in fire dynamics occur.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".