Evaluation of Selected Mitigation Strategies for Reducing Forest Fire-induced Risk
Bibliographic record
Abstract
The aim was to study post-fire perceptions of selected mitigation strategies for wildland fire- induced risks proposed in a previous scientific study for the communities situated within the forested areas. Consequently, we considered engaging relevant professionals in the Regional Municipality of Wood Buffalo (RMWB), Alberta who experienced the costliest wildland fire occurrences in Canadian history known as the 2016 Horse River Fire (HRF). To meet our goal, we formulated a questionnaire based on the scientific evidence presented in a previous study and con-ducted a structured survey. Our results revealed that 24 professionals participated in the survey during the June 2020-April 2021 period, providing a 32% response rate. We observed that a high percentage of the participants agreed (i.e., between 63% and 80%) with the proposed wildland fire-induced risk mitigation strategies, including the presence of no to little vegetation in the 30 m buffer zone from the wildland–urban interface (WUI), extending the 30 m buffer zone to 70 m from the WUI, constructing a 70 m width ring road around the communities, and parking lots of the social infrastructures in the fringe of the communities encountering to the forest. We also found other views, including the use of non-combustible and fire-resistant construction materials, and developing the 70 m buffer zone as a recreational space.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".