A Method to Precisely Measure Wind Turbine Pressure Disturbances, Including Noise
Bibliographic record
Abstract
Complex noises sources are not easily measured when the conditions constantly change. Wind turbines are an example of these challenging sources. Some say it’s like magic; now you hear them and now you don’t. Sound comes and goes and changes with distance, temperature, humidity, wind speed, wind direction, wind shear, thermal inversion, sound absorption, etc. Its annoyance and detectability may also be masked by various forms of intermittent background noise. There is also unwanted noise inherent with the measurement system itself, such as wind screen noise that needs to be separated from the sources of interest. These need to be identified such that only clean records where artifacts are not present are chosen and the sources of interest are analyzed. Being variables, many may, at times, have a cumulative effect on the sources, increasing their presence. Different receptors (humans and animals) react differently with different types of noise. Receptors that live under these conditions tend to experience annoyance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".