Prospective non-edible sources for biodiesel production: A comprehensive conventional and bibliometric review
Bibliographic record
Abstract
Anticipated increases in Nigeria's energy consumption correlate with urbanization, improved living standards, and population expansion. As society becomes more mindful of dwindling fossil fuel reserves and environmental issues, biodiesel emerges as a viable solution to meet future energy needs in both domestic and industrial sectors. Various feedstock alternatives exist for biodiesel production, with non-edible vegetable oils gaining attention due to their non-competitive nature with food crops. Nonetheless, a thorough examination of the feasibility of converting non-edible oils into biodiesel is necessary. This scrutiny is vital as biodiesel derived from any feedstock must adhere to ASTM and DIN EN specifications to ensure its suitability as a fuel. This research presents a detailed examination, both qualitatively and bibliometrically, of potential non-edible oils suitable for biodiesel production in Nigeria. Additionally, the aim is to evaluate the evolution of research outputs related to non-edible feedstocks over time, with a specific focus on the involvement of Nigeria as a nation, various institutions, journals, and authors. The analysis primarily focuses on original research publications and conference presentations, using search commands limited to article titles containing "Non-edible," "Seed oils," and "Nigeria." The results of the Scopus database searches were exported in BibteX format and further analyzed using R & R Studio. Findings indicate an increasing research interest over the past decade, with Jatropha, Neem, and Rubber seed oil appearing as the most frequently studied non-edible feedstocks in Nigeria. This study highlights the current research landscape and identifies promising directions for sustainable biodiesel development in the country.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.127 | 0.151 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".