Quantifying fire-specific smoke severity
Bibliographic record
Abstract
Rapidly changing wildfire regimes across the Western US has driven more frequent and severe wildfires, resulting in wide-ranging societal threats from the wildfires themselves and the smoke that they generate. However, common measures of fire severity focus on what is burned and do not account for the societal impacts of the smoke generated from each fire. We combine satellite-derived fire scars, air parcel trajectories from individual fires, and predicted smoke PM2.5 to link source fires to resulting smoke PM2.5 experienced by populations in the contiguous United States from April 2006-2020. We develop a new metric of fire-specific severity based on the cumulative population exposed to smoke PM2.5 over the duration of a fire. This measure is only weakly correlated with common measures of wildfire severity, including burned area, structures destroyed, and suppression cost. We find that while recent California fires contributed nearly half of the country's experienced smoke severity during our study period, the most severe individual fire was the 2007 Bugaboo fire in the Southeast. We estimate that a majority of experienced smoke PM2.5 comes from sources outside the local jurisdictions where the smoke is experienced, with 87% coming from fires in other counties and 60% from fires in other states. Our approach enables broad-scale assessment of whether specific fire characteristics affect smoke toxicity or impact, informs assessment of the cost-effectiveness of how suppression resources are allocated, and helps clarify the growing transboundary nature of local air quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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".