Wildfire simulations to protect rural communities and avoid dire evacuations
Bibliographic record
Abstract
Large and fast wildfires can have catastrophic effects on lives, property, and the environment. Changing wildfire regimes create more destructive wildfires globally, increasing the number of people placed in danger. Evacuations from wildfires are a dramatic, disruptive, yet sometimes necessary last step to ensure people are removed from danger when other layers of wildfire protection are insufficient. Evacuations that start too late may become dire, without enough time for people to evacuate safely, exposing them to the growing hazard. It is imperative that dire evacuations be avoided. This thesis provides a definition of dire evacuations and ways to plan against them, using calculations of wildfire spread. The core effort resides in trigger boundaries, imaginary lines around communities at risk of wildfires, which when crossed by the flames denote the last safe evacuation chance. For this purpose, the k-PERIL model was developed, the first to conduct probabilistic trigger boundary calculations, and applied to the communities of Roxborough Park in Colorado, USA, Mati in Greece and Fort McMurray in Canada. With wildfire spread rate having paramount importance on the validity of trigger boundaries, even state-of-the-art wildfire models were compared, first in a series of benchmark cases, then on probabilistic trigger boundaries, to estimate the effect of model choice on safety. An ensemble approach, considering all the models combined, is required for accurate trigger boundary calculation. Moreover, an effect of wildfire that is rarely considered in evacuations is smoke. A ground-level smoke dispersion model was developed, Galini, that accounts for downwind terrain, flaming and smouldering combustion. This thesis proposes a framework to assess the danger of wildfires on communities via ensemble modelling inferred trigger boundaries, using coupled wildfire, evacuation and ground level smoke modelling. This provides quality and actionable means for communities to plan and prepare themselves for evacuations and improve community safety.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".