Urban Air and Health Outcomes in Toronto, Canada
Bibliographic record
Abstract
The study investigates the associations of the onset of human health conditions with short-term exposure to ambient air pollution in Toronto, Canada. Urban air quality is influenced by various air pollutants, many of which are harmful to human health. This study focuses on the acute impact of these urban air pollutants in Toronto. The health conditions are measured as emergency department visits. Statistical models were constructed to assess the relative risks associated with the concentrations of these pollutants. The models were realized to 8 air pollutants and 18 strata (determined by sex, age, and season). Twelve disease categories, identified according to the International Classification of Diseases, 10th Revision (ICD-10), were used as health outcomes in the models. The results were compiled into matrices with 18 rows (strata) and 15 columns (lags) for each air pollutant (8 considered) and the 12 health categories classified by their ICD-10 codes. The results, in the form of the estimated coefficients and their standard errors, were used for the presented analysis. A series of graphs were produced to explore the effects of the selected air pollutants on health. The study indicates and supports the associations between ambient ozone levels and respiratory system diseases (ICD-10 codes: J00 – J99).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".