Agricultural Sources of Biofuels: Selection and Optimization of Energy Crops
Bibliographic record
Abstract
The increasing demand for sustainable and renewable energy sources has led to significant research into biofuels derived from agricultural sources. This study explores the selection and optimization of energy crops for biofuel production, focusing on their environmental impact, economic viability, and potential for large-scale implementation. Various energy crops, including first-generation food crops like corn and sugarcane, second-generation lignocellulosic biomass, and third-generation microalgae, are evaluated for their efficiency in biofuel production. The review highlights the advantages of using non-food crops such as Miscanthus, switchgrass, and sweet sorghum, which can grow on marginal lands and have high biomass yields. Additionally, the environmental benefits of using perennial grasses and short-rotation woody crops for soil improvement and carbon sequestration are discussed. The study also addresses the challenges associated with biofuel production, such as land use changes, carbon debt, and the need for advanced technologies to enhance yield and sustainability. Overall, this study provides a comprehensive analysis of the current state and future prospects of agricultural biofuels, emphasizing the importance of selecting appropriate energy crops and optimizing their cultivation to meet global energy demands sustainably.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".