Will It Burn? Characterizing Wildfire Risk for the Sagebrush Conservation Design
Bibliographic record
Abstract
Abstract For millennia, wildfire has helped shape the sagebrush biome of the western United States. Over recent decades, historical fire regimes have been altered by several factors, including contemporary climate and fuel conditions, leading to the loss or degradation of hundreds of thousands of hectares (ha) of sagebrush each year. In response to wildfire threats, extensive fuel treatment investments are proposed across the region. To help inform strategic and cost-effective investments, we conducted a quantitative assessment of wildfire risk for the sagebrush biome. We used a geospatial fire modeling approach, customized for the sagebrush biome, to estimate spatially explicit burn probability and expected average annual area burned within three Sagebrush Ecological Integrity classes under the Sagebrush Conservation Design: Core Sagebrush Areas (CSAs), Growth Opportunity Areas (GOAs), and Other Rangeland Areas. We further used indices of ecological resilience to disturbance and resistance to invasive grasses to characterize fire risk and recovery potential. Our approach indicates that nearly 530,000 ha are likely to burn in a typical contemporary fire year across the highest integrity Sagebrush Ecological Integrity classes (7% in CSAs and 31% in GOAs). Of the CSAs and GOAs likely to burn, nearly 9 000 and 66 000 ha, respectively, are expected to have low resilience or resistance and therefore highest loss potential. Cost-effective conservation investments should include wildfire protection for high-integrity sagebrush with low resilience or resistance. Protection objectives may be met with strategically placed fuel breaks intended to enhance fire prevention and containment efforts. Fuel treatments, including prescribed fire and mechanical activities outside of fuel breaks, are by contrast best suited for high-integrity areas with relatively high resilience and resistance. Those activities should be risk-informed and intended to maintain or improve ecological integrity and resilience to wildfire rather than to exclude fire altogether.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".