Bibliographic record
Abstract
Nigeria is one of the world’s leading producers of crude oil and yet it is a net importer of refined oil. Its average daily premium motor spirit (PMS) consumption is 30 million liters. At 100% capacity the nation’s refineries will produce 17.74 million liters of premium motor spirit daily. A shortfall of over 10 million liters per day exists. Recent estimates stipulate that Nigeria requires 20,000 MW of electricity per annum. The projected electricity supply from all sources (conventional and renewable) in Nigeria in the short (2007), medium (2015) and long term (2025) is estimated to be 7000 MW, 14000 MW and 29,000 MW respectively. Annual peak electricity demand in Nigeria will increase from 4,558 MW to 45,755 MW in 2050 at high growth rate and 4,558 to 19,599 MW for low growth. Nigeria’s electricity per capita of 27 W/person is not enough to light an electric incandescent bulb of 30 W. These provide enough justification for the use of renewable energy in Nigeria. The use of renewable energy in Nigeria is hindered by the lack of regulation, financial incentives, energy infrastructures, framework for sustainability and technical expertise. Overcoming these obstacles will require invigorating research and development in renewable energy technologies, training and education to build local technical capacity, diversification of energy mix and scaling up of renewable energy utilization, aggressive capacity building in all areas of energy development and creation of enabling environment that will attract investment in energy infrastructure. This paper discusses the obstacles and way forward in promoting renewable energy in Nigeria.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".