Multi-criteria decision-analysis on the circularity and life cycle assessment direction in grains, cereals, crops, and pulses industry: A Canadian Case Study
Bibliographic record
Abstract
This study extensively analyzes Life Cycle Assessments (LCAs) and circular economy solutions in the Canadian grains, crops, cereals, and pulse (GCCP) industry. The analysis reveals that corn, canola, and wheat are the most represented GCCP commodities in existing LCAs. At the same time, Saskatchewan and Ontario remain underrepresented relative to provincial GDP derived from GCCPs, particularly in oilseed and coarse grain assessments. Environmental LCAs (ELCAs) dominate the sector, with a limited focus on economic and social LCAs (s-LCA). The multi-criteria decision analysis (MCDA) technique of Combined Compromise Solution (CoCoSo) and entropy method was conducted to identify commodities and relevant circular economy solutions that require further LCA assessments. Using the proposed circular economy for research conceptual framework, it was determined that wheat, barley, and corn should be prioritized for future LCAs, and more extensive efforts should be conducted for soybeans. Consequently, single cell protein, corn bioethanol, and corn pellets emerged as promising circular economy solutions despite potential challenges in implementation. A correlation analysis revealed that hemp should prioritize LCA assessments of systems at the farm and processing gate, whereas corn and wheat should prioritize LCA modeling in circular innovations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".