Systematic assessment of triticale‐based biorefinery strategies: investment decisions for sustainable biorefinery business models
Bibliographic record
Abstract
Abstract Strategic investments in biorefinery projects are increasingly being made, and involve non‐traditional decision making, especially considering the technology and market risks involved. From the investor's perspective, the decision‐making process leading to product/process combinations for implementation as a biorefinery to achieve a sustainable business model and good economic returns is not obvious. Typical metrics used for investment decision making have some limitations regarding the recognition of acceptable technology risks relative to economic returns. They often do not appropriately consider factors and analyses related to, for example, environmental impact and the longer term competitive position of new product portfolios. The methodology presented in this article is an approach to identifying a ‘practical’ set of multi‐disciplinary decision‐making criteria to enable the selection of the preferred product/process biorefinery implementation strategy. The case of investment options in the triticale (X Triticosecale Wittmack) biorefinery is used as an example. Through this risk‐based methodology, technology risks as well as economic, environmental, and competitive benefits associated with different business model options are identified. This methodology leads to the development of a series of multi‐criteria decision‐making (MCDM) panels to define a set of practical criteria suitable for a final MCDM for the identification of triticale‐based biorefinery alternatives leading to long‐term and sustainable business models. © 2018 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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".