Quantifying dire evacuations in case of wildfire using trigger boundaries and case study of the 2018 Mati wildfire in Greece
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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".