Detecting instrumental icing using automated double anemometry
Bibliographic record
Abstract
Abstract In this paper, we propose several improvements to the standard double anemometry method for ice detection. In the double anemometry method, the wind speed measurement from a heated reference anemometer is compared with that of an unheated anemometer. A lower measurement from the unheated sensor suggests the presence of ice. First, we propose using a wind speed difference (not a ratio), because anemometers should not deviate significantly at any wind speed under ice‐free conditions. Second, the threshold should vary with ambient temperature to account for cup anemometer slowdown caused by thickening bearing grease. Finally, sensitive thresholds should be used to overdetect ice and false events removed during postprocessing. We created an algorithm to automatically determine the required thresholds and tested it on seven wind turbines during a full winter at a cold climate site. When compared with ice thickness measurements from cameras, the algorithm was equal to or outperformed the manual double anemometry method across all seven turbines.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| 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) | 0.001 | 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".