Road network density correlated with increased lightning fire incidence in the Canadian western boreal forest
Bibliographic record
Abstract
This paper quantifies the influence of anthropogenic linear disturbances on fire ignition frequency in the boreal forests of western Canada. Specifically, we tested if linear features increase the frequency of lightning fires, and whether this relationship is affected by spatial resolution. We considered fires that ignited between 1995 and 2002 within a ~67 000 km2 region of boreal mixed-wood forest in north-eastern Alberta where linear features are highly abundant and spatially heterogeneous. We constructed Poisson, Negative Binomial and Zero-Inflated Poisson models at two spatial resolutions (~10 000 and ~2400 ha), including covariates for linear feature densities, forest composition, weather–lightning indices and geography. We found a positive association between lightning fire frequency and road density; this association was consistent at both spatial resolutions. We suggest this occurs owing to increased availability of flammable fine fuels near roads. The effect was attributable neither to increased detectability of fires proximal to roads by human observers, nor to increased lightning strikes due to metallic infrastructure alongside roads or the topographic characteristics of road location. Our results suggest that, in the face of projected road developments in the region, the potential exists for important changes to the regional fire regime. Further research should elucidate the precise mechanisms in order to develop methods for mitigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".