An analysis of escaped fires from broadcast burning in the Prince George Forest Region of British Columbia
Bibliographic record
Abstract
Prescribed fire is an important silvicultural tool. Several factors, including changes in silvicultural techniques, regulations regarding the use of prescribed fire, air quality concerns, and concerns over escaped fires have led to a declining use of prescribed fire for silvicultural purposes in British Columbia. Using records from 351 prescribed fires that escaped control measures in the Prince George Forest Region, we examined the feasibility of using “Control Rank” from Muraro's Prescribed Fire Predictor model as an indicator of the risk of fires escaping from broadcast burning, and estimated the costs of suppressing these fires. We also outline the benefits (including potential dangers) of prescribed burning, and provide recommendations on how to estimate the probability, or the risk, of a fire escaping from a prescribed burn. We found that 84.3% of the escaped fires occurred within control ranks 5 and 6, and control ranks 7 and 8. These control ranks also had the largest and most costly fires to suppress, in some cases running in the hundreds of thousands of dollars. Our results suggest that control rank is a useful indicator of risk. However, we recommend that better records of burning conditions of all prescribed burns be kept to allow for more complete analyses of risk in the future. To reduce the risk of fires escaping from prescribed burning, factors that should be carefully considered are also outlined in this paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".