The spatially varying influence of humans on fire probability in North America
Bibliographic record
Abstract
Humans affect fire regimes by providing ignition sources in some cases, suppressing wildfires in others, and altering natural vegetation in ways that may either promote or limit fire. In North America, several studies have evaluated the effects of society on fire activity; however, most studies have been regional or subcontinental in scope and used different data and methods, thereby making continent-wide comparisons difficult. We circumvent these challenges by investigating the broad-scale impact of humans on fire activity using parallel statistical models of fire probability from 1984 to 2014 as a function of climate, enduring features (topography and percent nonfuel), lightning, and three indices of human activity (population density, an integrated metric of human activity [Human Footprint Index], and a measure of remoteness [roadless volume]) across equally spaced regions of the United States and Canada. Through a statistical control approach, whereby we account for the effect of other explanatory variables, we found evidence of non-negligible human–wildfire association across the entire continent, even in the most sparsely populated areas. A surprisingly coherent negative relationship between fire activity and humans was observed across the United States and Canada: fire probability generally diminishes with increasing human influence. Intriguing exceptions to this relationship are the continent's least disturbed areas, where fewer humans equate to less fire. These remote areas, however, also often have lower lightning densities, leading us to believe that they may be ignition limited at the spatiotemporal scale of the study. Our results suggest that there are few purely natural fire regimes in North America today. Consequently, projections of future fire activity should consider human impacts on fire regimes to ensure sound adaptation and mitigation measures in fire-prone areas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".