Clean air shelters: A climate-adaptive measure to protect children’s respiratory health during wildfire events
Bibliographic record
Abstract
Poor air quality is increasingly recognized as a significant threat that may especially impact disadvantaged children and communities. Over the next years and decades, climate change is expected to increase the likelihood of injury, disease, and death in part due to more frequent and intense heat waves, with a disproportionate impact on marginalized communities (1). As a result of the evolving climate crisis, Canada is likely to experience more frequent wildfires, resulting in severe environmental, economic and health consequences (1). Wildfire smoke is a complex mixture of particulate matter and gaseous pollutants. Fine particulate matter (PM2.5) and volatile organic compounds (VOCs) found in smoke reach the alveoli in the lungs and smaller particles can directly enter the circulatory system, driving systemic inflammation with negative cardiorespiratory and central nervous system impacts (2). Even in communities far downwind from a fire, poor air quality due to wildfire smoke can lead to significant health effects including emergency department visits, hospital admissions, and deaths, often due to cardiorespiratory ailments (3).
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".