A case study in Canada: Exploring research challenges of industrial wind turbines and health
Bibliographic record
Abstract
The topic of adverse health effects associated with industrial wind turbines (IWT) is controversial and debated worldwide. Some residents living in proximity to wind energy facilities report symptoms of sleep disturbance, annoyance, headaches, ear pain/discomfort, mood disorders, stress, cardiac and blood pressure effects, reduced quality of life, and other adverse effects. In some cases, research initiatives have been the result of individuals’ complaints. The research is challenged in part by the complexities and numerous variables associated with this subject. A range of IWT research approaches, sometimes in combination with each other, has been used including self-reporting surveys, investigations and acoustical measurements. On July 12, 2012, Health Canada announced its large scale cross-sectional randomized epidemiological wind turbine noise and health study. The research was conducted by Health Canada in two Canadian provinces. Health Canada indicated its study had limitations, would not be definitive, and would not permit any conclusions to be made with respect to causality. This presentation will explore some of the inherent challenges of studying health effects associated with wind energy facilities and will consider the role of those individuals reporting adverse health effects.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".