People are the problem and the solution: characterizing wildfire risk and risk mitigation in a wildland-urban intermix area in the Southern Gulf Islands
Bibliographic record
Abstract
People play an important role in both causing and mitigating risk in forest-urban intermix areas. We developed a wildfire risk assessment model that characterizes the nature and causes of wildfire risk and evaluates the effectiveness of risk mitigation strategies for a wildland-urban intermix area in the southern Gulf Islands, British Columbia, Canada. The risk maps produced highlight the significance of both human-caused fire ignitions and residential developments’ vulnerability to wildfire in producing wildfire risk. Wildfire managers should recognize that people, as much or more than biophysical factors such as fuel type or topography, drive wildfire risk in wildland-urban intermix areas such as those found in the Gulf Islands. As such, successful wildfire mitigation strategies should be designed to encourage changes in human behaviour as it relates to fire ignition and residential development. Furthermore, a successful risk assessment must involve stakeholders, building their capacity to undertake ongoing risk mitigation initiatives.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".