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Record W4393020974 · doi:10.26480/gwk.01.2023.99.107

SOLAR ENERGY ADAPTATION AND EFFICIENCY ACROSS DIVERSE NIGERIAN AND GLOBAL CLIMATES: A REVIEW OF TECHNOLOGICAL ADVANCEMENT

2023· review· en· W4393020974 on OpenAlexaboutno aff
Gabriel Gbenga Ojo, Oluwaseun Augustine Lottu, Tina Chinyere Ndiwe, Uchenna Izuka, Nwakamma Ninduwezuor -Ehiobu

Bibliographic record

VenueEngineering Heritage Journal · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsAdaptation (eye)Engineering physicsEnvironmental sciencePsychologyEngineeringNeuroscience

Abstract

fetched live from OpenAlex

Solar energy stands as a transformative force in addressing the world’s energy needs while mitigating the effects of climate change. This comprehensive review paper explores solar energy adaptation and efficiency across diverse climates, focusing on Nigeria, a nation grappling with energy access disparities and environmental challenges. The paper surveys technological advancements, climate-specific considerations, policy and regulation, environmental and economic impacts, challenges, and future directions in solar energy. Real-world case studies from rural Nigeria to Canada illustrate the versatility of solar technologies, while policy frameworks and regulatory approaches are analyzed to provide insights into effective solar energy promotion. The paper underscores the interconnected nature of environmental and economic benefits. It emphasizes the importance of tailored solutions and community engagement. Technological innovation, policy alignment, and capacity building address challenges such as intermittency, climate-related factors, and upfront costs. Future directions emphasize technological innovation, policy alignment, energy access, capacity building, international collaboration, public awareness, and monitoring and evaluation. As Nigeria and the world navigate a sustainable and solar-powered future, the sun emerges as a boundless energy source, illuminating homes, powering industries, and fueling economic growth while preserving the environment for future generations.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.030
GPT teacher head0.280
Teacher spread0.250 · 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 designOther design
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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