Effect of Recent Prescribed Burning and Land Management on Wildfire Burn Severity and Smoke Emissions in the Western United States
Bibliographic record
Abstract
Abstract Wildfires in the western US increasingly threaten infrastructure, air quality, and public health. Prescribed (“Rx”) fire is often proposed to mitigate future wildfires, but treatments remain limited, and few studies quantify their effectiveness on recent major wildfires. We investigate the effects of Rx fire treatments on subsequent burn severity across western US ecoregions and particulate matter (PM 2.5 ) emissions in California. Using high‐resolution (30‐m) satellite imagery, land management records, and fire emissions data, we employ a quasi‐experimental design to compare Rx fire‐treated areas with adjacent untreated areas to estimate the impacts of recent Rx fires (Fall 2018–Spring 2020) on the extreme 2020 wildfire season. We find that within 2020 wildfire burn areas where Rx fires were used prior to 2020, burn severity changed by −16% ( p < 0.001) and smoke PM 2.5 emissions changed by −101 kg per acre ( p < 0.1). Rx fires in the wildland‐urban interface (“WUI”) were less effective in reducing burn severity and smoke PM 2.5 emissions than those outside the WUI. Overall, Rx fires led to a net reduction of −14% in PM 2.5 emissions, including those from the Rx fires themselves. The proposed policy of treating one million acres annually in California could reduce smoke emissions by 655,000 tons over the next 5 years, equivalent to 52% of the emissions from 2020 wildfires. Our analysis provides comprehensive estimates of the net benefits of Rx fire on subsequent burn severity and smoke PM 2.5 emissions in the western US, an empirical basis for evaluating proposed Rx fire expansions, and valuable constraints for future modeling.
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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 | 0.001 |
| 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) | 0.001 | 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".