Investigating the Influence of Environmental Features on Air Pollution and Environmental Noise in Three Cities of Ontario, Canada
Bibliographic record
Abstract
This thesis examines the impacts of environmental features on air and noise pollution covariance in three different cities of Southern Ontario. More specifically, the objectives of the study were to (1) Evaluate the correlation and spatial relationship between nitrogen dioxide 2NO ), and environmental noise in Mississauga, Hamilton and London; (2) Analyze the influence of environmental features on the covariance of NO and NO2se levels in the study areas. A combination of descriptive and inferential bivariate, and multivariate statistical techniques were performed to develop local and global models of noise and air pollution covariance. This study found a substantial correlation between the NO 2nd environmental noise in Mississauga and Hamilton; however, the correlation in London was very low. According to the final Land Use Regression (LUR) model, variables like traffic volume, road length, and industrial land-use increase the NO a2d environmental noise in different cities, whereas Normalized Difference Vegetation Index (NDVI) helps to reduce the NO a2d environmental noise. This approach could be a good option for LUR modeling of environmental pollution in cities where extensive spatial monitoring is unfeasible, and more specifically, the predicted results of such a study may be applied to further health studies in the cities with the same land-use patterns.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".