Comparing landscape-based decision rules for placement of fuel treatments in the boreal mixedwood of western Canada
Bibliographic record
Abstract
The present study used a rule-based approach to prioritise locations of fuel treatments in the boreal mixedwood forest of western Canada. The analysis, which was conducted in and around Prince Albert National Park, Saskatchewan, was based on burn probability (BP) mapping using the Burn-P3 (Probability, Prediction, and Planning) model. Fuel treatment locations were determined according to three rule-sets and five fuel treatment intensities. Fuel treatments were located according to BP only; jurisdictional boundaries and BP; and non-flammable landscape features, BP, and fuel treatment orientation. First, a baseline BP map was created from the original (i.e. unmodified) fuels grid. Fuel treatments were then added to the selected areas and BP maps produced for each combination of rule-set and treatment intensity. BP values for the treated landscapes were compared with those of the baseline BP map. Results varied substantially among scenarios. Locating fuel treatments as a function of the jurisdictional boundaries and BP yielded the lowest reduction in BP. Results suggest that clumping fuel treatments within a limited area or using landscape features to maximise the large-scale spatial benefits of the fuel treatments can significantly reduce landscape-level BP. Although these two strategies may produce similar overall reductions in BP, their appropriateness and utility depend on management objectives.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".