Modelling the Effects of Timber Harvest and Harvest Site Selection on Burn Probability
Bibliographic record
Abstract
Climate change exacerbates wildfire risks globally, with projections indicating a significant increase in their frequency and severity. This study investigates the impact of timber harvesting on wildfire likelihood, focusing on the Mount Rose Swanson Sensitive Area (RSSA) in British Columbia, Canada. Using the Burn-P3 model, three scenarios were simulated: no harvest, harvest in high fire threat areas, and harvest in extreme fire threat areas. The Burn-P3 model simulates fire ignition and growth to calculate burn probabilities across the landscape. Results indicate that timber harvesting increases burn probability, with the highest probabilities observed in extreme fire threat zones. Within the RSSA, burn probabilities were consistently higher, reinforcing the vulnerability of this area. The spatial distribution of burn probabilities highlights the localized impact of harvesting on wildfire risk. These findings support the hypothesis that even a 5% harvest within the RSSA escalates wildfire likelihood. Notably, harvesting in extreme fire threat areas yields the greatest increase in burn probability. The implications of these results underscore the complex relationship between timber harvest and wildfire risk, necessitating careful consideration in forest management practices. Addressing these challenges requires a nuanced approach that balances economic interests with ecosystem resilience and wildfire mitigation strategies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".