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Record W2473098304

RENEWABLE ENERGY IN NIGERIA: A PEEP INTO SCIENCE, A CONCLUSION ON POLICY

2015· article· en· W2473098304 on OpenAlexaff
Temitope Tunbi Onifade

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsUniversity of British ColumbiaMemorial University of Newfoundland
Fundersnot available
KeywordsRenewable energyHydropowerNatural resource economicsFeed-in tariffEnergy policyFossil fuelEnergy subsidiesBusinessEconomicsRenewable energy creditEngineeringWaste management
DOInot available

Abstract

fetched live from OpenAlex

Many scholars and other stakeholders trace environmental problems facing many oil-rich jurisdictions to the current fossil fuel energy regime that oil-dependent economies rely on. In a quest for ways to reduce environmental problems, an examination of the situation and prospects of renewable energy as a complement, and where possible an alternative, to fossil fuel in an oil-dependent jurisdiction such as Nigeria is necessary. The literature on renewable energy and related topics has not sufficiently answered questions on why renewable energy has failed to thrive despite the interests governments have shown in it. Nigeria’s renewable energy sector can succeed only if stakeholders develop jurisdictionspecific policy frameworks addressing current renewable energy challenges. A peep into the scientific literature on renewable energy reveals facts and figures specific to different geographical zones in Nigeria, informing the right course of action for renewable energy policy and business in the country. Nigeria has commercially exploited hydropower and commenced exploiting solar energy, but has not exploited wind energy, bioenergy, and geothermal energy considerably despite their prospects across the geographical zones of the country. Nigeria should fill this gap by developing suitable jurisdictionspecific policy instruments and institutional frameworks.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.644
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.261
Teacher spread0.247 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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