Benchmarking and Multi-Criteria Decision Analysis Towards Developing a Sustainable Policy of Just in Time Production of Biogas in Nigeria
Bibliographic record
Abstract
Biogas is currently one of the most researched forms of renewable energy carried out by researchers because of its potential in replacing fossil fuel usage and aiding carbon-neutral energy production and consumption. Biogas Production has been successfully implemented in developed countries, which has generated sustainable energy for human comfort. Many developing nations that seek to engage in the production of biogas tend to struggle with the process. This paper aims to review the existing literature on benchmarking and multi-criteria decision analysis in developing a sustainable policy of biogas production in Nigeria. It is worthy of knowing that as of now, Nigeria as a nation does not have a policy governing the production of Biogas. The Government needs to apply some strategic steps to have the policy to guide the day-to-day running and develop a biogas production system, to improve the economic instability of energy generation in Nigeria. This research also discusses some significant ways to develop a sustainable policy for the just-in-time production of Biogas in Nigeria. After a thorough review of other literature, the study concluded that benchmarking and multi-criteria decision analysis is constructive in developing sustainable policy that will govern biogas plants and their production in Nigeria.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".