Exposure to fine particulate matter air pollution in Canada.
Bibliographic record
Abstract
BACKGROUND: Exposure to ambient fine particulate matter (PM2.5) has been associated with a greater risk of non-accidental, cardiovascular and respiratory mortality in Canada. Research based on Canadian cohorts suggests that exposure to PM2.5 varies by demographic and socioeconomic characteristics. Studies of NO₂, another pollutant, indicate that persons of lower socioeconomic status and some visible minority groups have greater exposure in urban centres. DATA AND METHODS: National residential PM2.5 was estimated from a ~1 km² spatial layer for respondents to the 2006 Census long-form questionnaire. Weighted PM2.5 estimates from personal-level estimates were determined for white, Aboriginal, visible minority and immigrant populations, as well as for socioeconomic groups (household income, educational attainment) and stratified by urban core, urban fringe and rural residence. Descriptive statistics were provided for selected comparisons. RESULTS: In Canada, PM2.5 exposure was 1.61 μg/m³ higher for visible minority (versus white) populations, and 1.55 μg/m³ higher for immigrants (versus non-immigrants). When the relatively high percentages of these groups in large cities were taken into account, exposure differences in urban cores were much smaller. Exposure among urban immigrants did not decrease substantially with time since immigration (< 0.5 μg/m³ between any two years). In urban cores, residents of low-income households had marginally higher exposure (0.56 μg/m³) than did people who were not in low-income households. INTERPRETATION: Differences between specific population groups in exposure to PM2.5 are due, at least in part, to higher percentages of these groups living in urban cores where air pollution levels are elevated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".