Editorial: Understanding and Communicating Wildland Fire Smoke Risk
Bibliographic record
Abstract
Globally, smoke from landscape fires-including wildland fire (encompassing wildfires and prescribed fires), agricultural burning, tropical deforestation fires, peat fires, and grass fires-is estimated to cause 339,000 deaths annually (1).Epidemiological, clinical, animal, and cellular studies support a positive association between short-term exposure to wildland fire smoke and premature death and respiratory disease, namely exacerbation of asthma, bronchitis, and pneumonia (2-4).Emerging data suggest that exposure to wildland fire smoke also increases the risk of clinical cardiovascular events, such as myocardial infarction, heart rhythm disturbances, stroke, out-of-hospital cardiac arrest, and acute heart failure (3, 5, 6).In addition, the potential for exposure to wildfire smoke during pregnancy to affect birth weight and prematurity is under exploration [(4, 7, 8) and references therein].Quantifying the economic costs of wildfire smoke exposure, in the United States (US), the direct burden has been estimated to be more than $11 billion US dollars per year for short-term exposures and more than $76 billion per year for long-term exposures (9).Similar impacts have been estimated in Canada (10) and Australia (11).The indirect economic burden is likely much higher.Wildfire smoke exposure risk is expected to increase in the future (12).Abatzoglou and Williams (13) determined that anthropogenic climate change and its effects on fuel aridity contributed to nearly a doubling of forest fire area in the western US over 1984-2015.On a global scale, scientists predict that climate change will result in "longer, hotter, and drier fire seasons" that increase the risk of severe wildfires and associated health impacts, including those from smoke exposure (14).Given the increasing public health threat of wildland fire smoke, environmental health scientists are challenged to define at-risk populations and provide data and tools to support health protective information and decisions.Public health and healthcare professionals rely on these scientific advances to communicate about risk and risk reduction strategies.Predicting wildland fire smoke exposure is critical to risk reduction, and this must occur in concert with communication about health protective resources.For example, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic was a critical co-occurring public health risk during the 2020 and 2021 wildfire seasons (15, 16).Considering wildfire smoke and SARS-CoV-2 together, specialized guidance was required to communicate the interplay between these two risks (e.g., https://www.cdc.gov/coronavirus
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.036 | 0.026 |
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".