Modeling and mapping dynamic vulnerability to better assess WUI evacuation performance
Bibliographic record
Abstract
Summary Wildland‐urban interface (WUI) fire incidents are likely to become more severe and will affect more and more people. Given their scale and complexity, WUI incidents require a multidomain approach to assess their impact and the effectiveness of any mitigation efforts. The authors recently produced a specification for a simulation framework that quantifies evacuation performance during WUI incidents including inputs from three core domains: fire development, pedestrian performance and vehicular traffic [26]. This framework could produce new insights by simulating evolving conditions of WUI incidents based on developments and interactions between the core components. Thus, it aims to overcome known limitations of previous approaches (eg, static assessment, single domain approaches, or lack of projection), as well as to provide explanatory insights into the outcomes produced by the simulation. The proposed framework would also advance geo‐spatial mapping of WUI incidents. The concept of dynamic vulnerability , , is at the core of the framework and is enabled by the integrated simulation framework and the emergent conditions predicted. This allows users to construct richer incident narratives from the perspective of specific locations or subpopulations, and also makes fewer simplifying assumptions regarding interactions between the three core domains.
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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".