Assessing the influence of environmental context on responses to noise exposure in the city of Toronto
Bibliographic record
Abstract
This thesis examines individual and community noise perception of environmental noise in three neighbourhoods in the city of Toronto. The significance of this research is based on a relative absence of literature on how noise sensitivity and annoyance are affected by non-acoustic factors such as the built environment, demographic, and socio-economic factors. Data from a neighbourhood noise survey (n=552) were combined with spatial data on exposures to noise. Bivariate analysis, multivariate regression, and classification and regression tree (CART) analysis were used. The results showed that participants in Downtown and Don Valley have similar noise responses (64% and 67% high annoyance) despite differences in noise exposure (LAeq 24h: 66.8 and 59.3). Estimation of Community Tolerance Levels (CTL) confirmed that participants exposed to lower sound levels have a lower tolerance of noise. Further results showed that a neighbourhood with high socioeconomic status and access to green space, and relatively low night time noise levels were still two times more likely to report high annoyance, compared with neighbourhood with moderate socio-economic status and lower access to green space. The results suggest that environmental context influences expectations and sensitivity to 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".