An evaluation of how nightly variations in wind turbine noise levels influence wrist actigraphy measured sleep patterns
Bibliographic record
Abstract
Health Canada’s Wind Turbine Noise and Health Study assessed self-reported and objective measures of sleep on a sub-sample of the study’s 1238 participants. The data analysis indicated that calculated long term outdoor wind turbine noise (WTN) levels up to 46 dBA did not have a significant influence on the evaluated measures of sleep (Michaud et al., 2016, Sleep 39, 97–109). A more refined analysis is being conducted to assess wrist actigraphy measured sleep patterns in relation to nightly variations in wind turbine operations. Variations in turbine operations (i.e., RPM and electrical power) were used to calculate WTN levels in 10-min intervals and time-synchronised with sleep watch data collected in 1-min epochs. The 10-min sound levels and daily sleep diaries (relied upon to adjust for closed or open windows) were used to estimate indoor A-weighted WTN levels. The correction factor used to obtain indoor sound levels was derived from a series of field measurements designed to investigate the outdoor to indoor sound pressure level difference on a representative sample of dwellings. The analysis is restricted to participants living between 0.25 and 1 km from wind turbines (116 males and 159 females). At these distances, WTN levels are the highest making it more likely to detect potential WTN impacts on sleep. Results will be presented for multiple measures of sleep in 10-min intervals and nightly averages for up to seven consecutive sleep nights.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".