Gender Differences in Chronic Exposure to Traffic-Related Air Pollution—A Simulation Study of Working Females and Males
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
The objective of this study was to identify spatial variability in exposure to outdoor traffic-related air pollution with specific emphasis on comparing exposure estimates for working females and males across a metropolitan area. A spatial exposure simulation model was used to estimate annual average exposure to traffic-related nitrogen dioxide for males and females reporting regular work in census tracts other than that of their residence, in Vancouver, British Columbia. The model produced estimates of annual average exposure in six microenvironments (home indoor, work indoor, other indoor, outdoor, in-vehicle to work, and in-vehicle other) using time-activity patterns and work flow data, for males and females in each of 382 census tracts. This allowed for the identification of spatial variations in exposure estimates for each gender, due to mobility within the study region. Indoor sources of nitrogen dioxide were not included in the simulation. No significant differences in estimated total exposure were found between working females and males in general. Small but observable spatial differences, however, were found between working females and males at the 90th percentile of the exposure distributions associated specifically with the work indoor microenvironment. These were highest in suburban areas (+3 μ g/m3 for females, relative to total exposures in the range of 26 to 37 μ g/m3 annual average hourly nitrogen dioxide). These results identify specific geographic locations in the study area where personal monitoring studies might be warranted and suggest that the inclusion of workplace locations in multivariate modeling could be useful to further understand differences in estimated exposures.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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