Wildfire Risk: Anchorage and Fairbanks, Alaska and Whitehorse, Yukon 2024
Bibliographic record
Abstract
While wildfires can be beneficial and part of a natural process, there have been numerous instances around the world, particularly in recent years, where wildfires have had devastating consequences for society. Weather conditions have created extreme wildfire behavior, resulting in speeds and intensities that can overpower suppression resources. It is ever more critical that communities and agencies take actions to mitigate and prevent wildfire disasters. We have developed a tool that enables wildfire practitioners to assess the risk of wildfire to structures in a straightforward, rapid, and affordable manner. The approach leverages information often collected by communities (e.g., building footprints, zoning) and available vegetation datasets. In conjunction with local wildfire management regulations, our project also used wildfire exposure to help identify wildland-urban interface boundaries. We used this approach on three communities in the Arctic (Anchorage and Fairbanks, Alaska, and Whitehorse, Yukon) to assess wildfire risk. We determined that there is considerable wildfire risk in urban Arctic communities, with a greater percentage of structures at high or very high risk in Fairbanks (26 percent (%)) and Whitehorse (22%) compared to Anchorage (14%). Combining local wildfire management practices with wildfire exposure is a successful way to identify meaningful Wildland Urban Interface (WUI) boundaries, which are essential for obtaining mitigation funds and planning. The key to producing updatable wildfire risk and vulnerability maps is accurate, up-to-date information on vegetation, building footprints, and zoning. With this information and the tool outlined here, communities and agencies have a way to inform community wildfire protection plans and identify impactful mitigation actions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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; both teacher heads agree on what is shown here.
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".