Industrial wind turbines and adverse health effects: Where we are, where we need to go, and the need for regulations and predictive models to recognize human physiology
Bibliographic record
Abstract
Since our initial study was published (Nissenbaum et al., 2012), work in several areas of human physiology has begun to elucidate the precise mechanisms by which sleep disturbances result in adverse health effects, over both short and longer durations. These include impaired neuronal connections in the learning brain, altered genetic expressions impacting the immune system, and correlations between poor quality sleep and MRI-measured atrophy of the brain over a mean period of 3.5 years. Additionally, fMRI has demonstrated brain responses to sounds with frequencies as low as 8 Hz. At lower frequencies, somatosensory mechanisms are now thought to play a role, in addition to auditory. Local regulations regarding noise (Soundscape) limits and methods of measurement were designed prior to current understandings of human sensory and reactive physiology. Instrumentation and modeling geared towards satisfying those regulations are by implication lacking because they do not capture or predict physiological responses to IWT noise. According to the principles of soundscape, and given the subtleties of human physiology, humans remain the best instruments available for detecting objectionable noise and identifying adverse health effects. Regulations, measurement methods, and predictive models must adapt to current understandings of human physiology to best protect human populations.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".