Effects of Lakes on Wildfire Activity in the Boreal Forests of Saskatchewan, Canada
Bibliographic record
Abstract
Large lakes can act as firebreaks resulting in distinct patterns in the forest mosaic. Although this is well acknowledged, much less is known about how wildfire is affected by different landscape measures of water and their interactions. Here we examine how these factors relate to historic patterns of wildfire over a 35-year period (1980–2014) for the boreal forest of Saskatchewan, Canada. This includes the amount of water in different-sized neighborhoods, the presence of islands, and the direction, distance, and shape of nearest lake of different sizes. All individual factors affected wildfire presence, with lake sizes ≥5000 ha and amount of water within a 1000-ha surrounding area the most supported spatial scales. Overall, wildfires were two-times less likely on islands, more likely further from lakes that were circular in shape, and in areas with less surrounding water. Interactive effects were common, including the effect of direction to lake as a function of distance from lakeshore and amount of surrounding water. Our results point to a strong, but complex, bottom-up control of local wildfire activity based on the configuration of natural firebreaks. In fact, fire rotation periods predicted for one area varied more than 15-fold (<47 to >700 years) depending on local patterns in lakes. Old-growth forests within this fire-prone ecosystem are therefore likely to depend on the surrounding configuration of larger lakes.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".