Bibliographic record
Abstract
Abstract Wildfires spread along trajectories set by a coincident wind direction. Despite the highly directional nature of wildfire threats to public safety, landscape fire risk assessments are typically omnidirectional. We used a simple metric of landscape fire exposure to develop a systematic and standardized approach for assessing directional vulnerability to wildfire within a circular assessment area centered on locale of interest. First, we defined a viable wildfire trajectory by analyzing 573 sample trajectories delineated within the burned areas of historical fires in the province of Alberta, Canada. On average, sample trajectories intersected locations assessed as having high wildfire exposure for 79% of their length. We, therefore, defined a viable fire trajectory as one with at least 80% of its length traversing high exposure. Using this criterion, we assessed the viability of directional trajectories representing possible wildfire pathways from outlying landscape areas into a locale of interest centered within a roughly 70,000-ha circular assessment area. At each central assessment point (i.e., community centroid), we delineated 360 linear trajectories into the community at 1° directional intervals. Each 15-km trajectory was divided into three 5-km segments for analysis (inner, middle, and outer). The length of each directional trajectory segment that intersected high exposure was computed for all 1080 directional segments in each community assessment area. In total, we evaluated 986,040 directional segments for 913 communities in the province. Communities exhibited highly unique and varied patterns of directional vulnerabilities to wildfire encroachment. Of the communities analyzed, 136 had at least one continuous viable trajectory spanning the full 15-km distance from the community centroid, and 211 communities had at least one continuous viable trajectory spanning 5–15 km from the community centroid. We developed customized rose or polar diagrams for displaying spatially referenced directional vulnerabilities to wildfire for a given community and combined results for all 913 analyzed communities to assess regional vulnerabilities within administrative management areas (i.e., Forest Areas). Potential applications of our directional assessment method are discussed, including prepositioning and prioritizing limited fire suppression resources, planning fuel reduction treatments, proactively identifying candidate locations for operational activities, assessing transportation network vulnerabilities during evacuations, and scenario planning.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 0.005 |
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".