Using a change in percent highly annoyed with noise as a potential health effect measure for projects under the Canadian Environmental Assessment Act
Bibliographic record
Abstract
Health Canada is in the process of developing a document, Guidance for Environmental Assessment-Health Impacts of Noise (Guidance) on how to assess noise impacts in environmental assessments. The guidance document is needed to assist Health Canada in providing consistent expert advice on the health effects of project noise, when requested under the Canadian Environmental Assessment Act (CEAA). Differences exist between various noise mitigation criteria used in environmental assessments from across Canada. Therefore, the first step for Health Canada to provide consistent advice is to establish quantitative criteria for adverse health effects as a function of project-related long-term changes in noise. The criteria should be based on scientific research that has demonstrated a reasonable cause-effect association between an adverse impact on public health and well-being and community noise exposure. This paper shows that: (i) there is a substantial amount of community-based social and socio-acoustic research and (ii) precedent from U.S., European and International standard and policy setting bodies, to justify the use of a change in percentage highly annoyed with noise (%HAn) as one of the health endpoints for an environmental assessment. Furthermore, viewing high noise annoyance as an adverse health effect is consistent with Health Canada's definition of "health". This paper also shows that %HAn is preferable as a long term endpoint than the use of noise complaints. To add to this, there have been recent nation-wide Canadian social surveys on high noise annoyance that further support its use as an adverse health effect to be considered in Canadian environmental assessments.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".