The City of Penticton’s comprehensive approach to wildfire risk reduction
Bibliographic record
Abstract
From 2017 to 2023, British Columbians experienced four record-breaking wildfire seasons, resulting in reduced air quality, mass evacuations and the destruction of homes, properties and livelihoods. Wildfire risk reduction is vital to breaking the sequence of disaster that has befallen such communities as Kelowna, BC in 2003, Ft. McMurray, AB in 2016, and Lytton, BC in 2021. As the City of Penticton ('the City') is located in a wildfire-prone environment, its Fire Department, FireSmart Team and Emergency Program have worked closely together to facilitate a proactive and comprehensive approach towards reducing the impacts of wildfire on Penticton's neighbourhoods, businesses and residents through a variety of wildfire mitigation initiatives. This paper discusses the City's efforts to achieve a holistic wildfire risk management plan through alignment with the seven disciplines of FireSmart and the four pillars of emergency management, namely: the use of education; land use planning and development considerations; vegetation management; emergency planning; and cross training and interagency cooperation. The paper describes the challenges the City has faced, as well its successes, and provides recommendations to help other local authorities reduce the risk of wildfire in their communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".