A Case Study of Birch Pollen and the Synergy with Environmental Factors: Relation to Asthma in Montreal, Canada
Bibliographic record
Abstract
Environmental factors such as air pollution are known to exacerbate respiratory illness and increase the overall health risk. However, on a daily or seasonal basis, the relation between air pollutants, weather and a disease such as asthma is not clear. When combined with aeroallergens such as birch pollen and under specific weather conditions, synergistic effects may increase symptoms of respiratory illness and morbidity and then reveal interesting links with environmental factors. Hence, it is important to improve the understanding of pollution-pollen-weather and broaden the public health message. Combined analysis and model simulation of aeroallergens, air pollution and weather as presented here is important to correctly evaluate health burdens and allow a better forecast of the potential health risk. However, analyzing the combined effects of several environmental factors is not well understood and represents a challenging task. This paper shows: (1) the results of data analysis performed in Montreal for asthma hospitalization in relation to complex synergistic environmental factors, and (2) model simulation of birch pollen using a coupled weather-air quality model (GEM-MACH) compared with model-data fusion of classical chemical species (e.g., near-surface ozone, nitrogen dioxide and fine particulate matter) in order to evaluate spatiotemporal vulnerable zone for asthma health risk.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".