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Record W2942800074 · doi:10.1504/ijesd.2019.10020922

Bioenergy technology development in Nigeria - pathway to sustainable energy development

2019· article· en· W2942800074 on OpenAlexaff
P.O. Babalola, R.O. Leramo, Mfon Udo, Oluwaseun Kilanko, Olukunle C. Olawole, J.A. Oyebanji, Sunday O. Oyedepo, Ahme A. Attabo, T. Adekeye, Israel Dunmade

Bibliographic record

VenueInternational Journal of Environment and Sustainable Development · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsMount Royal University
Fundersnot available
KeywordsRenewable energyBioenergySustainable developmentBusinessContext (archaeology)Energy supplyNatural resource economicsEconomic growthEnvironmental economicsEconomicsEnergy (signal processing)EngineeringPolitical scienceGeography

Abstract

fetched live from OpenAlex

In Nigeria, high and outrageous energy cost constitutes a serious challenge to all aspects of the economy. And this has been a subject of concern in the country over a period of time. A dependable and renewable energy source is of utmost desire to strengthen the nation's economy and this urgent need cannot be overlooked. As a result of the erratic and expensive power supply to the national grid, it has become ultimately necessary to search for other inexpensive sources of energy to meet the ever increasing energy needs for rural and urban dwellers. In view of the above energy situation in Nigeria, this paper aimed at carrying out a comprehensive review on bioenergy technology option as a pathway to achieving sustainable energy development in the country. The potential, current progressive stages, and prospects of bioenergy conversion techniques, in the Nigeria context, are discussed. The barriers to bioenergy technology development in Nigeria with possible solutions are also presented.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.188
Teacher spread0.185 · 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.

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

Citations10
Published2019
Admission routes1
Has abstractyes

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