Characterizing and predicting ultrafine particle counts in Canadian homes, schools, and transportation environments : an exposure modeling study with implications in environmental epidemiology
Bibliographic record
Abstract
Airborne particulate matter has a negative effect on respiratory health in both children and adults, and the ultrafine fraction of particulate air pollution is of particular interest owing to its increased ability to cause oxidative stress and inflammation in the lungs. In this investigation, our objective was to characterize ultrafine particle (UFP) counts in homes, schools, and transportation environments and to develop models to predict such exposures. A number of important determinants of UFP exposures were identified including ambient temperature and wind speed for transportation environments, outdoor UFPs for classrooms, and electric oven use, cigarette smoking, indoor relative humidity, and volume for homes. In general, our findings suggest that classrooms and transportation environments may be more suitable for UFP exposure modeling than homes. However, large diesel vehicles and in-school UFP sources had a negative influence on model performance, and future studies should include factors such as traffic counts/characteristics, vehicle ventilation settings, and in-school UFP sources to improve the predictive performance of the models presented. Nevertheless, our findings are encouraging in that we demonstrate for the first time the possibility of obtaining UFP exposure estimates for homes, schools, and transportation environments using models based on ambient weather data and other readily available determinant information. As such, similar models may be useful in population-based studies interested in the potential health effects of UFP exposures.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".