The 2024 Roxborough Park Wildfire Evacuation Drill : Lessons Learned after Planning, Running, and Studying a Community Evacuation Exercise
Bibliographic record
Abstract
In June 2024, the community of Roxborough Park, Colorado conducted a wildfire evacuation exercise (or drill for short) that was observed by a team of researchers. This collaborative effort involved residents, community organizers, first responders, emergency managers, and researchers. The drill aimed to test and refine evacuation protocols, communication strategies, and coordination among various stakeholders and gave the opportunity to researchers to collect information related to human response in a wildfire evacuation. The present contribution describes the community as well as the roles and activities of the parties involved, the drill itself, followed by lessons learned. Drill participants practiced real-time decision-making, route navigation, and emergency response actions in response to a hypothetical wildfire threat. The exercise highlighted strengths in community readiness and identified areas for improvement, such as traffic management and information dissemination. Feedback from participants and observers was collected to inform future planning and training. The drill underscored the importance of community engagement and interagency cooperation in mitigating wildfire risks and ensuring the safety of residents. This proactive approach could serve as a model for other Wildland-Urban Interface (WUI) communities striving to enhance their wildfire resilience.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".