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Record W2179012232 · doi:10.5539/jsd.v8n9p106

Wind Energy Assessment at Bafoussam, Cameroon

2015· article· en· W2179012232 on OpenAlexvenueno aff
Talla Pierre Kisito, Bawe Gerard Nfor, David Yemélé, Ghogomu Patrick Ndinakie

Bibliographic record

VenueJournal of Sustainable Development · 2015
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
Fundersnot available
KeywordsWeibull distributionWind speedMathematicsWind powerAnimal scienceMorningStatisticsMeteorologyPhysicsAtmospheric sciencesEngineeringElectrical engineeringBiology

Abstract

fetched live from OpenAlex

<p>Three-hourly wind speed data measured using the Beaufort scale at a height of 10m, from 6am to 6pm local time (5 periods per day), was obtained from the Bafoussam Airport. It was analyzed using the Weibull and Rayleigh probability density models and wind rose plots. It was determined that the lowest wind speeds (most calms) were observed during the first period (6am) and the highest at 3pm (fourth period). The very low morning wind speed adversely affected the daily mean wind speed. Better, but still poor, power density results were obtained at this fourth (3pm) period. The monthly and yearly mean speeds varied between 1.9 and 3.1m/s and with very low standard deviations. The wind rose plots also showed that all the significant winds fell in the first quadrant (NE) and predominantly on angle 10<sup>o</sup> with some discernibly on 20<sup>o</sup> and 30<sup>o</sup>, only. Three goodness-of-fit tests: the chi square, coefficient of determination or R<sup>2</sup> and root mean square error, showed the Weibull to be a better fit to the wind regime than the Rayleigh model. The shape parameters were always greater than the scale parameters. Results show that, using the Weibull parameters, the power density of Bafoussam falls in the category 1 of the wind energy resource group and hence is not a very good wind energy exploitable candidate.</p>

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.237
Teacher spread0.222 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations6
Published2015
Admission routes1
Has abstractyes

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