Assessing private land fuel management treatments in the wildland urban interface of British Columbia's southern interior
Bibliographic record
Abstract
The problems of fire in areas where forest and infrastructure meet (the wildland urban interface) are becoming better understood as a mounting body of research grows against the backdrop of climate change, forest fuel build-up and the expansion of human settlement into rural areas. The Province of British Columbia has a history of catastrophic wildland fires. Following the events of the 2003 fire season, fuel management emerged as a key recommendation to mitigate the risk of wildland fire in rural areas. Although the overall effectiveness of the Provincial fuel management strategy will continue to be the subject of debate, there is consensus that at-risk private landowners need to take responsibility for their safety. Fuel management on Crown land can provide a model for landowners to implement mitigation strategies on their own properties, but are homeowners using that model? This study investigated if homeowners in wildland urban interface areas within the Kamloops Fire Zone of British Columbia are using adjacent Crown land fuel management treatments as a template for conducting mitigation work on their own property. This was accomplished using a two-part study. The first part of the study involved an in situ measurement of Crown and private land parcels using hazard assessment criteria modified from FireSmart Canada. Hazard assessment scores of fuel-managed public lands and adjacent private lands were similar, suggesting that private land owners were reproducing mitigation measures on their own properties. Fuel management reduced the hazard assessment scores of crown and private lands in the areas studied and there was variability in fuel management treatments. The second component of the study involved a mail-out survey to wildland urban interface residents to support the findings of part one and gain a better understanding of the human dimension of fuel management treatments by clarifying the motives, trends and obstacles that may be in place. Residents of the wildland urban interface were aware of the risk of wildland fire impacting their communities. Most residents were familiar with the FireSmart program. Education continues to be an important component of any forest fuel mitigation effort and residents in fuel managed areas generally felt a greater sense of community than those residing in untreated areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".