Managing Smoke Risk from Wildland Fires: Northern California as a Case Study
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Smoke fine particulate matter (PM 2.5 ) from increasing wildfires in the western United States threatens public health. While land managers often prioritize reducing wildfire risk in the wildland-urban interface, the impact on regional air quality from mitigating wildfire spread has been less explored. We developed a framework to quantify wildfire contributions to smoke exposure and assess targeted land management strategies. This data-driven approach integrates fire emissions and smoke transport to generate a smoke risk index at 0.25° × 0.25° resolution. We deploy the smoke risk index in an online tool, enabling stakeholders to analyze smoke risk under various scenarios of burned area, fuel consumption, and land management. Using Northern California as a case study, we estimate that in 2020, targeted land management in the 15 highest risk areas (∼3.5% of the total) could have reduced smoke exposure by 17.6%. However, most prescribed burns conducted from 2017 to 2020 did not overlap with these high-risk zones. Our framework also estimates excess deaths from smoke PM 2.5 exposure, attributing ∼36,400 (95% CI: 25,400–47,200) deaths nationally to western US fires in the year following the 2020 fire season. Our adaptable tool can incorporate higher-resolution data sets and help stakeholders prioritize fuel treatment and fire suppression to mitigate smoke exposure risks.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".