Can changing the timing of outdoor air intake reduce indoor concentrations of traffic‐related pollutants in schools?
Bibliographic record
Abstract
Traffic emissions have been associated with a wide range of adverse health effects. Many schools are situated close to major roads, and as children spend much of their day in school, methods to reduce traffic-related air pollutant concentrations in the school environment are warranted. One promising method to reduce pollutant concentrations in schools is to alter the timing of the ventilation so that high ventilation time periods do not correspond to rush hour traffic. Health Canada, in collaboration with the Ottawa-Carleton District School Board, tested the effect of this action by collecting traffic-related air pollution data from four schools in Ottawa, Canada, during October and November 2013. A baseline and intervention period was assessed in each school. There were statistically significant (P < 0.05) reductions in concentrations of most of the pollutants measured at the two late-start (9 AM start) schools, after adjusting for outdoor concentrations and the absolute indoor-outdoor temperature difference. The intervention at the early-start (8 AM start) schools did not have significant reductions in pollutant concentrations. Based on these findings, changing the timing of the ventilation may be a cost-effective mechanism of reducing traffic-related pollutants in late-start schools located near major roads.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".