Identifying and analyzing spatial and temporal patterns of lightning-ignited wildfires in Western Canada from 1981 to 2018
Bibliographic record
Abstract
To assess wildfire risk linked to lightning-caused wildfires, the present study tests the spatial- and temporal-scale distribution variability of lightning fires in Western Canada between 1981 and 2018. We examined clustering, trends, distances between clusters, and the fire season. For this study, the nearest neighbours, K-function, Moran's I, Mann–Kendall, and the Getis-ord Gi* statistics were used. These statistics were visualized by a Space Time Cube model with a hexagon grid. Lightning-ignited wildfires cluster spatially up to 270 km with an observed overall nonsignificant decreasing trend for the number of fires. Overall, northeastern Alberta, central Saskatchewan, and southeastern British Columbia show clustering of lightning fires. In June, there is significant clustering in northwestern and eastern Alberta, while in July fires cluster in northeastern Alberta and in southeastern British Columbia. In August, fire clusters occurred only in southeastern and south-central British Columbia. These results highlight regions that are experiencing persistent lightning fire clustering activity. This provides a focal point to assess wildfire risk to communities and values at risk, while informing local and regional management agencies in preparedness, resource capacity management, and detection. Additionally, it provides a baseline for future research into biophysical modelling of wildfire initiations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".