The spatially varying influence of humans on fire probability in North America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it