MEASURING CYCLISTS' EXPOSURE TO TRAFFIC EMISSIONS ACROSS URBAN CYCLING FACILITIES
Bibliographic record
Abstract
This paper seeks to examine the relationship between traffic emissions and cyclists’ exposure to air pollution across a variety of cycling facilities within the Island of Montreal. The concentration of ultra-fine particulate matter (UFP) was measured at each second along a set of cycling routes. Two pairs of research assistants cycled on 25 unique routes over a five-week period. Most routes were measured on four occasions: during the morning and evening peak periods on two separate days. Each route was approximately 25 ± 3 km for a total of approximately 600 km, covering nearly all 425 km of cycling facilities on the Island of Montreal as well as other common cycling corridors. A map of air quality across this network was generated for the morning and afternoon periods indicating significant differences in air pollution levels with the morning period associated with worse UFP levels. This is attributed to traffic flows which are higher during the morning as well as lower ambient temperatures. Preliminary results show a significant correlation between cyclists’ exposure to UFP and measured traffic volumes (p<0.05), but even stronger correlation between exposure and the volume of trucks (p<0.01), indicating that vehicle composition may be an instrumental component of traffic data collection. Furthermore, results show that UFP exposure is inversely correlated to the distance between the bike path and the road (p<0.05) and that on average, bike lanes separated by a lane of parked cars have UFP levels 28.5% lower than without (p<0.05).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".