Weibull Distribution Function for Wind Energy Estimation of Gharo (Sindh)
Bibliographic record
Abstract
Weibull distribution function is fitted to a measured wind speed data set at mast height of 30 m and Gharo-Sindh (Pakistan) is selected as a case site under study. Wind speed data recorded in one minute interval for the year 2004 is used to estimate Weibull parameters (k and c). Weibull parameters are calculated using Modified Maximum Likelihood Method (MMLM), Maximum likelihood Method (MLM) and Method of Moment (MoM) and the results obtained are compared. Kolomogorov-Smirnov test, Root Mean Square Error (RMSE) and R2 tests are performed to test the goodness-of-fit of the Weibull distribution function. The analysis is based on recorded monthly and yearly wind speed data. Goodness-of-fit tests indicate a better performance of MMLM and MLM as compared to MoM. Wind power density is estimated for the site under study using MMLM and Weibull function estimator. A lowest Weibull mean wind speed of 3.73 m/s in the month of October and highest value of 7.90 m/s for August are observed and correspondingly power densities of 80.95 W/m2 and 425.87 W/m2 are obtained. Descriptive statistics for the measured wind speed data is also evaluated.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".