Spatial bottom‐up controls on fire likelihood vary across western North America
Why this work is in the frame
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Bibliographic record
Abstract
The unique nature of landscapes has challenged our ability to make generalizations about the effects of bottom‐up controls on fire regimes. For four geographically distinct fire‐prone landscapes in western North America, we used a consistent simulation approach to quantify the influence of three key bottom‐up factors, ignitions, fuels, and topography, on spatial patterns of fire likelihood. We first developed working hypotheses predicting the influence of each factor based on its spatial structure (i.e., autocorrelation) in each of the four study areas. We then used a simulation model parameterized with extensive fire environment data to create high‐resolution maps of fire likelihood, or burn probability (BP). To infer the influence of each bottom‐up factor within and among study areas, these BP maps were compared to parallel sets of maps in which one of the three bottom‐up factors was randomized. Results showed that ignition pattern had a relatively minor influence on the BP across all four study areas, whereas the influence of fuels was large. The influence of topography was the most equivocal among study areas; it had an insignificant influence in one study area and was the dominant control in another. We also found that the relationship between the influence of these factors and their spatial structure appeared nonlinear, which may have important implications for management activities aimed at attenuating the effect of fuels or ignitions on wildfire risk. This comparative study using landscapes with different biophysical and fire regime characteristics demonstrates the importance of employing consistent methodology to pinpoint the influence of bottom‐up controls.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.045 |
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