Exposure of the Canadian wildland–human interface and population to wildland fire, under current and future climate conditions
Bibliographic record
Abstract
In Canada, recent fire seasons have demonstrated the threat of wildland fire in the wildland–human interface (WHI) areas, where forest fuels intermingle with or abut housing, industry, and infrastructure. Although fire activity is expected to increase further in the coming decades as a result of climate change, no WHI-specific estimates of wildland fire exposure are currently available. This study combines spatial and demographic information sources to estimate the current and future wildland fire exposures, as reflected by fire return intervals (FRI) of WHI areas and populations across Canada. The WHI covers 17.3% of the forested area in Canada. Within the WHI, we found that 19.4% of the area currently experiences FRI of ≤250 years, but by the end of the century, this could increase to 28.8% under Representative Concentration Pathway (RCP) 2.6 and to 43.3% under RCP 8.5. Approximately 12.3% of the Canadian population currently live in the wildland–urban interface (WUI), which includes 32.1% of the on-reserve First Nations population. Currently, 17.8% of the on-reserve WUI population is exposed to FRI of ≤250 years, compared with only 4.7% of the remaining WUI population. By 2100, these proportions could reach 39.3% and 17.4%, respectively, under the less optimistic climatic scenarios (RCP 8.5).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".