Systematic assessment of triticale‐based biorefinery strategies: market competitive analysis for business model development
Bibliographic record
Abstract
Abstract Triticale (X Triticosecale Wittmack) is a high‐productivity cereal crop that holds great promise as an industrial feedstock for agricultural biorefineries, as it can grow on marginal lands. Several product derivatives can be envisioned; however, they need to be systematically explored and assessed using a sustainability perspective, in order to define a business model that would lead to a long‐term competitive position. This study presents a competitive analysis of triticale‐based product‐process alternatives defined on ethanol, polylactic acid (PLA), and thermoplastic starch polymer blends (TPS/PLA) product platforms. As part of the analysis framework, we sought to identify a set of important market‐oriented criteria for multi‐criteria decision‐making (MCDM), prior to an overall sustainability assessment in which techno‐economic and environmental criteria are considered as well. From an initial set of necessary competitiveness criteria, three ‘most‐important’ competitiveness criteria for the sustainability assessment of the PLA platform were identified including competitive access to biomass, competitiveness on production costs, and the potential to manage market price volatility. Certain key factors have been highlighted for each platform as an outcome of the competitiveness assessment, such as the impact of value‐added co‐products on the competitive position of commodity‐based product portfolios, and the advantage of combining grain and straw process lines for specialty‐based product portfolios leading to improved competitive potential. © 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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".