Bibliographic record
Abstract
Impacts of wildland-urban interface (WUI) fires continue to rise in the U.S., as evidenced by the string of devastating and record-breaking events occurring since 2017. As seen in several events in recent years, WUI fires can impact communities quickly, leaving little to no time for civilians to evacuate. Numerous events have also occurred internationally, including Australia, Canada, Chile, Greece, and Portugal. One example is the Camp Fire that occurred on November 8, 2018, in Butte County, CA. The fire resulted in 85 fatalities and the destruction and damage of over 18 000 buildings, destroying over 90 % of the buildings in the town of Paradise. Following the fire, NIST initiated a case study to document and analyze fire spread and behavior, notifications, evacuations, and defensive actions to support preparedness for future WUI fires. The NIST Camp Fire case study has highlighted a number of potential challenges that intermix communities may face during WUI fire events. The purpose of this report is to use the lessons learned from the NIST Camp Fire case study to present a methodology and other considerations about WUI fire incidents that can be used by small and intermediate-sized WUI communities to help develop notification and evacuation plans. The proposed methodology considers the spatial and temporal components of fire spread and the resulting impacts of fire on evacuation to develop an evacuation triangle that can be used as the foundation for notification and evacuation decisions by emergency managers. This report provides communities a path forward for assessing, planning, and implementing a notification/evacuation plan that leverages pre-fire conditions, local knowledge, and during event information to enhance the life safety of civilians and first responders. While additional research will provide further refinements, specifically in the areas of weather forecasting, fire spread modeling, and evacuation modeling, the proposed system outlines a path for community leaders to effectively work with first responders before a fire to assess and prepare the community for WUI fire events that can strike with little or no notice. The methodology provides community leaders with a temporal context of WUI fire events that will enable them to better evaluate different hazard reduction and risk management strategies to enhance the life safety of residents and first responders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".