Addressing Traffic Related Air Pollution: Local Public Health Challenges
Bibliographic record
Abstract
The growing body of evidence on the health impacts of traffic related air pollution (TRAP) exposures and the widespread population exposures to TRAP have clear implications for local public health practitioners. Vancouver Coastal Health (Vancouver, BC) has been exploring ways to reduce population exposures to TRAP within the major municipalities in its jurisdiction. This includes exploring the feasibility of setbacks for buildings that house vulnerable populations (daycares, long-term care, hospitals, and schools), and promoting the health impact assessment process to address TRAP and other transportation related health impacts. Throughout this exploratory work many hurdles have been encountered from defining “high” TRAP exposure areas for building setbacks to challenges with weighing the pros and cons of daycare siting. Our ongoing exploration of and approach to this complex challenge will be discussed.The Health Protection team within Vancouver Coastal Health includes both Environmental Health and Community Care Facilities Licensing (child care, long-term care). Within Environmental Health the Healthy Built Environment team works in collaboration with local governments to create environments that promote and protect health.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".