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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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; both teacher heads agree on what is shown here.
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".