On the probabilistic distribution of wind speeds: theoretical development and comparison with data
Why this work is in the frame
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Bibliographic record
Abstract
The probabilistic distributions of wind speed are a critical piece of information needed in the assessment of wind–energy potential, and have been conventionally described by various empirical correlations. Among the empirical correlations, the Weibull distribution has been most popular due to its ability to fit most accurately the variety of wind–speed data measured at different geographical locations in the world. This study develops a theoretical approach to the analytical determination of the wind–speed distributions through the application of the Maximum Entropy Principle (MEP). Although it has been used in a variety of fields, this is the first time MEP has been applied to the wind energy field. Under the MEP, the maximisation of Shannon's entropy is carried out subject to the conservation of mass, momentum and energy associated with the wind flow. It is shown that the present theoretical predictions agree very well with a variety of the measured data from different sources and have better accuracy than the Weibull distributions.
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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 it