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Record W4285300864 · doi:10.47743/pesd2022161010

Minimizing the cost of energy consumption for public institutions in Nigeria

2022· article· en· W4285300864 on OpenAlexaff
Lukman Ahmed Omeiza, Абул Калам Азад, Kateryna Kozak, Ukashat Mamudu, Aikhonomu Osayemen Daniel

Bibliographic record

VenuePresent Environment and Sustainable Development · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsConcordia University
Fundersnot available
KeywordsEnergy consumptionConsumption (sociology)Public institutionBusinessInstitutionEnvironmental economicsPhotovoltaic systemEnergy (signal processing)Argument (complex analysis)Energy policyEconomicsRenewable energyNatural resource economicsEngineeringPolitical scienceSociologyLawSocial science

Abstract

fetched live from OpenAlex

Energy and humanity are superimposed. Its importance to humankind and indispensable nature to the world cannot be overemphasized. Public institutions and households need energy to carry out their daily activities. The increase in the demand for energy in public institutions can be traced to technological advancement, which has resulted in a sharp rise in energy consumption, increasing the energy monthly utility bill astronomically. These have put pressure on public institutions in Nigeria and across the globe, as most are heavily indebted to energy companies. Due to constant electric power failure in Nigeria, public institutions rely heavily on diesel generators to argument their daily energy need. Cost minimization and sustainable clean energy have become a priority for the managers of our public institutions. This study was carried out to establish the exact amount of energy consumption at the Federal Polytechnic Bida, Nigeria, and fashion out ways of reducing the cost burden for the institution.The study was achieved by installing "Efergy Meter" to understudy the energy audit of the institution. Based on the result obtained, the exact amount of energy consumption was established. The solar photovoltaic (P.V.) system was considered a clean and cost-effective option for our public institutions in the long term.

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 categoriesInsufficient 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 score0.813
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.032
GPT teacher head0.230
Teacher spread0.198 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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