Quantifying dire evacuations in case of wildfire using trigger boundaries and case study of the 2018 Mati wildfire in Greece
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wildfire evacuation is a life-saving measure of last resort, but delays can lead to dire outcomes, putting people at risk of fire entrapment. The success or failure of an evacuation depends on the relative speeds of the wildfire and the evacuation, and this varies across communities and wildfires. Despite the importance of understanding this dynamic, no formal framework exists to define or quantify a dire evacuation, and the term is often used informally in technical literature. This paper proposes a method for quantitatively defining dire evacuations using trigger boundaries. Trigger boundaries are perimeters indicating that the time left before a wildfire reaches a community equals the time required for evacuation. By treating both wildfire spread and evacuation times as probabilistic variables, we introduce an evacuation safety factor to assess the likelihood of a dire evacuation. This factor ranges from 1 (no risk of dire evacuation) to 0 (100% risk). Trigger boundaries thus define the latest wildfire location with a low risk of a dire evacuation. The 2018 Mati wildfire in Greece illustrates this approach. In Mati, fast-moving flames led to a dire evacuation with 104 fatalities. Our model shows that its evacuation safety factor was well below 1 even from the moment the wildfire was detected, indicating a high probability of dire evacuation from the start. This methodology can be applied to past wildfires for forensic analysis or to guide future evacuation strategies. Identifying trigger boundaries allows communities to prepare more effectively for wildfire threats and enhance their safety plans.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it