Using a change in percent highly annoyed with noise as a potential health effect measure for projects under the Canadian Environmental Assessment Act
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Health Impacts o f 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 o f project noise, when requested under the Canadian Environmental Assessment A ct (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 o f 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 o f 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 o f "health" .This paper also shows that %HAn is preferable as a long term endpoint than the use o f 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it