Traffic Emissions and Air Quality near Roads in Dense Urban Neighborhood
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A traffic simulation was developed for a dense neighborhood in the city of Montreal, Quebec, Canada (8,656 links), and was linked with a regional traffic assignment model, which was used to determine the travel demand originating in and destined for the study area. With a version of the U.S. Environmental Protection Agency's Motor Vehicle Emissions Simulator model fit with local data, traffic emissions for each link were simulated by the use of instantaneous speed profiles. Emissions of greenhouse gases (GHG), oxides of nitrogen (NO x ), and carbon monoxide (CO) were modeled under a range of regional and local policies, including fleet renewal, street closures, reduced demand, reduced internal car trips, and reduced through traffic. Several traffic scenarios were modeled in the traffic assignment and simulation models to represent these policies. Because of the high congestion levels in the neighborhood under base case conditions, limited networkwide reductions in emissions were observed, except in the scenario that aimed to reduce through traffic (29% reduction in GHG emissions compared with that in the base case scenario). Significant changes in the spatial patterns of emissions were detected. Average and instantaneous speed-based estimates were also compared; the average speed mode tended to overestimate total emissions as network speeds decreased. Finally, dispersion modeling was conducted along selected corridors to evaluate the effects of different scenarios on air quality. The study found significant increases in air pollution as a result of the street closure scenario and significant decreases with the reduced through traffic scenario.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it