Exploring the potential of upcycling craft brewers spent grain in Winnipeg, Manitoba
Bibliographic record
Abstract
Food upcycling is a circular economy method of reducing food waste by adding value to foods that conventionally would have gone to waste. This project explores the potential of upcycling brewers spent grain (BSG) to human food products in Winnipeg, MB to think global and act local, primarily considering The Paris Agreement, The United Nations 17 Sustainable Development Goals, and planetary boundaries. Data was derived from a comprehensive literature review, a website review of 1165 unique Canadian craft breweries, 168 responses to a Canada-wide craft brewer survey, and 12 semi-structured interviews. The data analysis shows that with the current brewer interest, there is sufficient spent grain volumes to upcycle at small scales locally and suggests that large scale/industrial supply is possible with the participation of all local craft breweries. The suggested framework pick-up route would lower spent grain transportation emissions of all local breweries by 25-50%. The final recommendation report concludes that the majority of local spent grain currently goes to animal feed, but Winnipeg does in fact have potential to upcycle BSG locally while minimizing or removing barriers to upcycle for all affected parties, while increasing sustainable operations of local food and beverage companies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".