Advances in the use of genetically modified plant biomass for biodiesel generation
Bibliographic record
Abstract
Abstract Biodiesel is a low‐carbon‐intensity renewable fuel with up to 99% lower greenhouse gas emissions than petroleum‐based diesel. The use of oil crops for biodiesel is under critical examination. It is expensive and suffers from the food versus fuel risk/benefit problem. Consequently, many countries (e.g. Malaysia and countries in the EU) are scaling back the use of oil crops as feedstock for biofuel production. The limitations of these traditional crops are leading the renewable fuels industry to consider innovative, sustainable, and profitable biomass‐based platforms. Plant genetic engineering and other new breeding technologies are essential for developing such biomass‐based platforms because they enhance plant tolerance to abiotic and biotic stresses, resulting in higher feedstock yields, greater net energy gain, and the generation of high‐value co‐products. We review and summarize the recent improvements of oil crops through plant genetic engineering that may increase widespread and cost‐effective production of biodiesel and value‐added co‐products for green chemistry applications. © 2017 Society of Chemical Industry and John Wiley & Sons, Ltd
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".