Methodology for biorefinery portfolio assessment using supply‐chain fundamentals of bioproducts
Bibliographic record
Abstract
Abstract Given the emergence of innovative processes in recent years for the manufacture of bioproducts from second‐generation biomass, a range of unique biorefinery strategies are likely to be implemented by forest product companies in the coming years. No matter what biorefinery strategy is employed, to compete in the longer term, it will be critical to have a supply‐chain adapted to the targeted products. In order to meet customer needs and at the same time be cost‐competitive, there are trade‐offs to be made between responsiveness and efficiency in several areas of the supply chain, such as production and customer service. This paper reviews supply‐chain characteristics and competitive factors for various bioproducts. An approach that can be used by decision‐makers during early‐stage design is presented, suitable for screening‐out less promising options based on their supply‐chain characteristics. Fundamental aspects such as the differentiation of products, their possible green advantage, biomass procurement, and process characteristics are discussed within five categories: bioenergy, biofuels, commodity biochemicals, fine and specialty biochemicals, and biomaterials. Several examples of biorefinery strategies are discussed to illustrate these concepts.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".