Estimating Retrospectively Exposures to Outdoor Air Pollution at the Intraurban Scale in an Ontario Cohort Study
Bibliographic record
Abstract
ISEE-0575 Background: We assessed retrospectively intraurban exposures in support of an ongoing cohort study in Ontario of long-term air pollution among 646,000 adults identified from the Canadian T1 family tax file (T1FF) database. Methods: We assessed exposures at addresses of subjects (six-character postal codes) in 1982, as listed on the T1FF file, who lived in Hamilton, Toronto, and Windsor, Ontario using: 1) small-scale concentrations of NO2 in 2002 which were determined from land-use regression models based on dense exposure surveys conducted that year; 2) land use and vehicular traffic (1982 and 2002); and 3) average daily concentrations of NO2 measured at monitoring sites (1982 and 2002). For each city separately, we extrapolated to 1982 the land-use regression maps developed in 2002 as follows: 1) annual mean concentrations from monitoring sites were regressed against spatial characteristics of land use and traffic in 1982 and 2002, respectively; 2) the residuals from the regression models were interpolated using inverse distance weighting; 3) point-by-point concentrations were estimated by adding the interpolated residuals to the linear predictors from these regression models; 4) ratios of estimated annual concentrations between 1982 and 2002 were calculated at every location; and 5) the land use regression map in 2002 was multiplied by these ratios to obtain small-scale concentrations of NO2 in 1982. Results: The annual mean concentrations of NO2 in Hamilton, Toronto, and Windsor decreased between 1982 and 2002 by 30%, 16%, and 30%, respectively. The reductions were not spatially homogenous; in Toronto, for example, the highest decline occurred in the central areas (∼20%) as compared to the periphery of the city (∼5%). Detailed maps will be presented. Conclusion: The ambient concentrations of NO2 decreased over time. There were important spatial variations in the reductions within cities and accounting for this will improve the accuracy of estimates of relative risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".