Development of a Model for the Establishment of a Hydro Electric Power Generating Plant
Bibliographic record
Abstract
Nigeria as a nation has suffered a lot when it comes to the availability of electricity. A clear comparison between this nation’s electric power supply and other countries revealed the present incessant electric power supply in the country. The average power per capital (watts per person) in the United States is 1,377 Watts. In Canada, it is as high as 1,704 Watts per person and in South Africa; it is 445 Watts per person. The average power per capital in Australia is 1,112 Watts and in New Zealand it is 1,020 W per person. Whereas, the average power per capital (watts per person) in Nigeria is 14 W person. The power system structure is characterized with a lot of faults and outages. These electric power problem has destroyed the industrial processes in the country. As a result, unemployment has increased in the country. As at February, 2020, according to the Federal Government of Nigeria, the number of unemployed youths in the country is 23 million. Data from the International Transparency in the United State stated that there are 40 million unemployed youths in the country. This has increased crime rates among the youths. The country experience a high level of hardship, insecurity and socio-economic disorder as results. Therefore, there is an urgent need to solve this incessant supply of electric power in the country. Hence, a detail study of Akure132/33kV substation Network of the Benin Electricity Distribution Company under which there are 84,264 customers was carried out.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.002 |
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".