Daily Time in Transportation and Traffic by Urban Canadians
Bibliographic record
Abstract
Traffic is an ever-present issue in urban centers and exposure to traffic and traffic-related air pollution is associated with wide-ranging health effects. Results from the Canadian Human Activity Pattern Survey (CHAPS) 2 were used to evaluate daily time spent in transportation and traffic by urban Canadians. It was estimated that Canadians spend 4-7% of daily time in on- or near-road locations, mainly from time spent in a vehicle with smaller contributions from time spent in active transportation. Furthermore, when in a vehicle, 44-61% of the target population was in moderate to heavy traffic. In addition, 11-22% of the target population was in moderate to heavy traffic while engaged in active transportation. Over 60% of the target population reported living near a busy roadway, which varied with income level and city of residence. People living near major roadways also spent more daily time in the vicinity of moderate to heavy traffic. Over 55% of the target population ≤18 years reported attending a school or daycare in close proximity to a busy roadway, with little variation based on income level and city. Overall, these results indicate that urban Canadians spend a considerable amount of daily time in transportation and traffic-influenced microenvironments. Quantitative measures of time spent in these microenvironments provide support for initiatives or strategies designed to mitigate population exposure to traffic and traffic-related air pollution.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".