Human and infrastructure exposure to large wildfires in the United States
Bibliographic record
Abstract
Abstract An increasing number of wildfire disasters occurred in recent years in the U.S. Here, we demonstrate that cumulative primary human exposure – population residing within large wildfires’ perimeters – was 594,850 people from 2000-2019 across the Contiguous U.S. (CONUS), 82% of which occurred in the Western U.S. Primary population exposure increased by 125% in CONUS in the past two decades, noting large uncertainty ranges. We show that population dynamics from 2000-2019 alone accounted for 24% of the observed increase rate in human exposure, whereas increased wildfire extent drove a majority of the observed trends. Additionally, we document widespread exposure of roads (412,155 km) and transmission powerlines (14,835 km) to large wildfires in CONUS, with an increased rate of 58% and 70% from 2000-2019, respectively. Our findings highlight the benefits of mitigation and adaptation efforts to help societies cope with wildfires.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".