Canada’s changing climate: Visualizing wildfires in Lebel-sur-Quévillon
Bibliographic record
Abstract
Wildfires in Canada are common occurrences, but as climate change drives rising fire activity and intensity, remote wildland-urban interface (WUI) sites are increasingly under threat. This paper uses a single event—the 2023 Québec wildfires prompting evacuation of Lebel-sur-Quévillon (LSQ)—as a methodological pilot project for visualizing dynamic wildfire behaviour and the traces megafires leave behind. The paper utilizes two methods, moving between general fire behaviour principles at a distance and their specific impacts on the ground. First, we developed physical models that draw from fire science experimentation methods to visualize principles of fire behaviour. Second, we completed fieldwork in LSQ a year post-fire; we then identified and mapped three sites of WUI significance: a firebreak, a power substation and a logging clean-up site. Combined, the work makes legible variables that drive volatile dynamic fire spread while revealing the wide range of conditions that fall within the WUI purview.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".