Assessing the impact of stand-level harvests on the flammability of forest landscapes
Bibliographic record
Abstract
The harvesting of forest stands can reduce landscape flammability by fragmenting fuel continuity in ways that make it difficult for fires to spread and by providing firefighters with fuel discontinuities they can use as anchor points for suppression operations. We describe a methodology for assessing the impact of harvesting designated forest stands on landscape flammability and expected losses. We combine assessments of the probability that fires will be ignited at any point on the landscape with probabilistic predictions concerning how long escaped fires will burn and how they will spread. Shortest path methods are used to identify critical paths that link potential ignition points with values at risk. We then rank stands with respect to their ability to disrupt those critical paths and thereby reduce landscape flammability and fire losses. We describe how we applied our methodology to a 12 964-ha forested area of boreal forest in the province of Alberta, Canada. Our results indicate that the crucial stands in our study area, those that have the most significant impact on landscape flammability and fire loss, tend to be those that are flammable and located on or close to critical paths that link areas where fires are most likely to occur with values at risk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".