Bibliographic record
Abstract
Abstract To date, the majority of world's primary energy is derived from fossil fuels. However, the fossil fuel recourses are in an inevitable decline as energy demand continues to grow exponentially with population growth, urbanization, and improved standards of living. Crude oil prices have recently risen several times and their current annual volatility exceeds 30%. The potential scarcity of fossil fuels has prompted a global search for alternative energy resources. Biodiesel fulfills the major requirements for production of alternative fuels such as feedstock availability, technical feasibility, and economic competitiveness. Together with other renewable biofuels, the use of biodiesel as a substitute of fossil‐based fuels is expected to reduce the dependence on imported petroleum and associated political and economic vulnerability, decrease greenhouse gas emissions, and revitalize the economy. The objective of this article was to provide an update of the most recent technological advancements toward clean and sustainable biodiesel production through a thorough overview of biodiesel feedstocks, most promising transesterification processes, and opportunities for glycerol utilization for value‐added products. A critical analysis of the techno‐economical barriers and environmental challenges that need to be addressed in future R&D efforts toward commercialization and establishment of a sustainable and cost‐efficient biodiesel production is provided.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.014 |
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".