Wind data analysis of Silchar (Assam, India) by Rayleighs and Weibull methods
Bibliographic record
Abstract
In this study, wind energy potential of Silchar, the Southern part of Assam, India was analyzed for the period of five years from 2003 - 2007. The wind velocity was recorded diurnally, which was averaged over 24 h in a day. The sampling was done after every 3 h. Diurnal wind speed variation shows the actual picture of wind regime of a place. The average wind velocity in Silchar is about 3.11 kmph, which is considerably low. The wind power density of the place was determined on monthly basis of the period from 2003 - 2007 and it showed that the average power density is found highest during the month of March to April, when it becomes around 40 watt/sq.m. The probabilities of observing various wind velocities were determined using Weibull and also Rayleigh’s distribution functions. The results between these two distributions were compared. Key words: Weibull distribution, Rayleigh’s distribution, wind velocity, power density.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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 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".