Wind Data Collection and Analyses at Masdar City for Wind Turbine Assessment
Bibliographic record
Abstract
Wind turbine technology has improved dramatically in the last two decades and their deployment and implementation increased by 20-25% annually. Wind is neither chaotically generated nor is based on random phenomenon. Wind is predictable to greater extend. Current predictive models lack the validation against collected historical data. In this work a 50m meteorological tower was installed at Masdar City for continuous collection of annual wind data records. Data is sampled at 10 minutes sampling rate using Campbell 1000 data logger connected to cup vane anemometry at 5 different heights to estimate the boundary layer profile. Collected and estimated wind energy density was below 120Watt/m 2 suggesting low wind area and undermining the feasibility of wind turbine implementation in the city. Data is analyzed for their first (mean and standard deviation) and second moments (correlation and spectrum) and found to vary considerably in scale and time suggesting simultaneous time and scale analysis. Wavelet analysis is used to study the intermittency of the wind data and quantify their intermittency factor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".