Developments in crops and management systems to improve lignocellulosic feedstock production
Bibliographic record
Abstract
Abstract There is an urgent need to develop viable, renewable, sustainable energy systems that can reduce global dependence on fossil fuel sources of energy. Biofuels such as ethanol are being utilized as blends in surface transportation fuels and have the potential to improve sustainability and reduce greenhouse gas emissions in the short term. Bioethanol, the most widely used liquid biofuel, is currently produced by converting sugars or starches from feed crops into ethanol. Use of this fuel source displaces and draws water consumption away from agricultural crops, increases soil erosion by shifting land from perennial grasses to annual crops, and increases use of fertilizers and insecticides. In contrast, bioethanol made from lignocellulosic biomass feedstocks does not have these limitations and in addition, offers a larger resource base: the amount of cellulosic material available for potential use vastly outweighs the amount of available starch‐based feedstock. Therefore, bioethanol from lignocellulosic biomass has attracted considerable interest from biofuel developers. This review is an update of some developments to optimize cellulose extraction from feedstock crops and to improve crop yields and logistics. It concludes that agricultural and forestry systems that incorporate lignocellulosic biomass crops can be designed for improved ecological function and energy use efficiency. Development of crops that have both desirable cell‐wall traits and high biomass productivity under sustainable low‐input conditions can significantly enhance the economics and efficiency of the conversion process. Optimizing the logistics of moving feedstock from field or forest to bio‐refinery can significantly reduce costs of using lignocellulosic feedstocks. © 2013 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".