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Record W2342727879 · doi:10.5897/jmer.9000051

Wind data analysis of Silchar (Assam, India) by Rayleighs and Weibull methods

2010· article· en· W2342727879 on OpenAlexvenueno aff
Rajat Gupta, Agnimitra Biswas

Bibliographic record

VenueMechanical Engineering Research · 2010
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
Fundersnot available
KeywordsWeibull distributionRayleigh distributionWind speedWind powerEnvironmental scienceMeteorologyWind profile power lawAtmospheric sciencesRayleigh scatteringStatisticsMathematicsProbability density functionPhysicsEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.050
GPT teacher head0.370
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2010
Admission routes1
Has abstractyes

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