Wind Turbine Public Safety Risk, Direct and Indirect Health Impacts
Bibliographic record
Abstract
Wind turbines are often perceived as benign. This can be attributed to the population majority dwelling in urban locations distant from most wind turbines. Society may understate the risk to individuals living near turbines due to an overstatement of the perceived benefits of turbines, and an understatement of the risk of injury from falling turbine parts, or shed ice. Flaws in risk calculation may be attributed to a less than fully developed safety culture. Indications of this are the lack of a comprehensive industry failure database, and safety limits enabling the industry growth, but not protective of the public. A comprehensive study of wind turbine failures and risks in the Canadian province of Ontario gives data to enable validation of existing failure models. Failure probabilities are calculated, to show risk on personal property, or in public spaces. Repeated failures, and inadequate safety separation show public safety is not currently assured. A method of calculating setbacks from wind turbines to mitigate public risk is shown. Wind turbines with inadequate setbacks can adversely impact public health both directly from physical risk and indirectly by irritation from loss of safe use of property. Physical public safety setbacks are separate from larger setbacks required to prevent irritation from noise and other stressors, particularly when applied to areas of learning, rest and recuperation. The insights provided by this paper can assist the industry to enhance its image and improve its operation, as well as helping regulators set safety guidelines assuring protection of the public.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".