Burn-P3 modelling of fire behaivour on Rose Swanson Mountain, British Columbia before and after harvesting
Bibliographic record
Abstract
It has been long known that wildfire and harvesting can have a relationship. However, this relationship is not straightforward and well understood. In this paper, we hope to quantify the impact of proposed harvesting on the wildfire severity and probability on Rose Swanson Mountain. Rose Swanson Mountain is a small mountain in South Central BC near the town of Armstrong used by the locals for its bountiful outdoor recreation. In early 2020 British Columbia Timber Sales added part of the Rose Swanson Mountain Sensitive Area to its list of soon-to-be harvested areas. This paper illustrates the research done in BurnP3 and ArcPro to simulate and measure fire in a pre and post logged Rose Swanson. After 5,000 iterations of the BurnP3 model using the pre-logging fuel type data, the fire probability ranged from 0% to 49.7%, with a mean probability of 6.45% across the landscape. After 5000 iterations of the BurnP3 model using post logging fuel types on Rose Swanson, the burn probability ranged from 0-49.34%, with a mean of 6.43% across Rose Swanson Mountain. This indicated the fire probability across the mountain and little increase as a whole. However, an apparent increase in fire probability was present in the close vicinity to the logged areas. Additionally, the pre-logged average fire intensity values on Rose Swanson ranged from 0 to 60,599 kW/m with a mean value of 2,841 kW/m, while the post-logged ranged from 0 to 122,849 KW/m with a mean of 3,183 kW/m. This difference was clear in the maps produced by BurnP3. While the results were insightful, many limitations were noted, including poor fuel type accuracy, limited model run count, and low-resolution data. The research offers valuable insight into the wildfire and harvesting relationship and sets the toon for further research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".