Minimizing the cost of energy consumption for public institutions in Nigeria
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".