The Relationship Between Wildfires and Respiratory Health
Bibliographic record
Abstract
Wildfires have been increasing in length and frequency since the mid-1980s, which emit pollutant matter that can adversely impact human health, specifically exacerbating negative respiratory impacts (Lipner et al., 2019). Although many studies examine the respiratory impacts of worsened wildfire conditions, a limited number take place outside of the western United States, occur over prolonged periods, and look at how air quality is changing due to warming climates. The objective of this study is to investigate how air quality health index (AQHI) values have changed over a period of two decades (January 1, 2001 to December 31, 2021) in the Great Plains Air Zone (GPAZ) in southern Saskatchewan and the potential respiratory implications of AQHI changes. The study uses a mixed methods approach: time series analysis, correlation analysis, and regression analysis to analyze AQHI and a literature review to study respiratory implications of AQHI increases. We found a significant increase in AQHI over time of 19.3% (p < 0.0001, n = 7308) and three main outcomes for respiratory data: (1) individuals with respiratory conditions have increased susceptibility to worsened air conditions, (2) elevated AQHI values cause increases in healthcare admittances for respiratory conditions such as asthma and COPD, and (3) women and individuals in the age ranges of 15-65 are particularly susceptible to respiratory outcomes from wildfire smoke. The study provides areas for future research, including the implementation of apps to track respiratory outcomes, the impact of other risk factors on respiratory health, and the effects of AQHI on Indigenous populations.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".