Burden of Disease from Traffic Noise Quantification of Healthy Life Years Lost in Toronto, Canada
Bibliographic record
Abstract
This paper explores how traffic noise in Toronto can lead to Ischemic Heart Disease (IHD), high levels of annoyance, and high levels of sleep disturbance. Local traffic noise data was combined with methods for predicting how daytime (Lden) and nighttime (Lnight) decibel levels impact years of life lost (DALYs) as a result of the heath outcomes. The methods were borrowed from European studies as there has yet to be any North American studies on this public health issue. The result for Toronto was a total of 28,380 annual DALYs, meaning this number of years of healthy life were lost as a result of traffic noise in Toronto causing IHD, high levels of annoyance, and high levels of sleep disturbance. A geographic information system (GIS) was used to spatially analyze the traffic noise and see where the high decibel levels were located as well as where concentrations of DALYs for each health outcome were located across Toronto at a dissemination area level. The goal of this paper is to highlight the importance of environmental noise pollution as a public health issue for Toronto and North America more generally, demonstrate how to calculate noise levels into a burden of disease metric, and provide spatial insights at a local scale to aid in addressing the health impacts of noise.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".