Biorefinery systems – potential contributors to sustainable innovation
Bibliographic record
Abstract
Abstract Sustainable biorefineries have a critical role to play in our common future. The need to provide more goods using renewable resources, combined with advances in science and technology, has provided a receptive environment for biorefinery systems development. Biorefineries offer the promise of using fewer non‐renewable resources, reducing CO2 emissions, creating new employment, and spurring innovation using clean and efficient technologies. Lessons are being learned from the establishment of first‐generation biofuel operations. The factors that are key to answering the question of biorefinery sustainability include: the type of feedstock, the conversion technologies and their respective conversion and energy efficiencies, the types of products (including coproducts) that are manufactured, and what products are substituted by the bioproducts. The BIOPOL review of eight existing biorefineries indicates that new efficient biorefineries can revitalize existing industries and promote regional development, especially in the R&D area. Establishment can be facilitated if existing facilities are used, if there is at least one product which is immediately marketable, and if supportive policies are in place. Economic, environmental, and social dimensions need to be evaluated in an integrated sustainability assessment. Sustainability principles, criteria, and indicators are emerging for bioenergy, biofuels, and bioproducts. Practical assessment methodologies, including data systems, are critical for both sustainable design and to assure consumers, investors, and governments that they are doing the ‘right thing’ by purchasing a certain bioproduct. If designed using lifecycle thinking, biorefineries can be profitable, socially responsible, and produce goods with less environmental impact than conventional products … and potentially even be restorative!. Copyright © 2010 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".