Distributions of wind speed in a northern environment
Bibliographic record
Abstract
For the assessment of wind energy potential, it is necessary to know the distribution of wind speed at sites of interest. Usually, a probability density function (pdf) is fit to observed wind speed data. In the present work we study the distribution of wind speed in the province of Québec (Canada), a region with enormous wind energy potential and which is observing increasing interest in this renewable energy source. To identify the most appropriate distribution for the selected stations we use the method of L-moment ratio diagram which was recently proposed in the literature. The advantages of this approach are that it is simple to apply and it allows an easy comparison of the fit of several pdfs for several stations on a single diagram. Previous studies have shown that it is frequent for wind speed data to present bimodal distributions for which conventional one-component pdfs are not appropriate. On the other hand, mixture distributions were proven to be efficient to model such distributions. Homogeneous and heterogeneous mixture distributions are also used in the present study to model wind speeds and the advantages of these mixture distributions are illustrated and discussed.
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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".