Take an Upcycling Mindset To Your Next Conference
Bibliographic record
Abstract
More than half of all food produced by Canadian farms ends up in the landfill each year — 58 percent to be precise. That's even higher than in America, where approximately 38 percent of food produced — or 80 million tons (enough to feed 149 billion people) — meets the same end. These are some shocking stats that Christine Couvelier, global culinary trendologist for Culinary Concierge (Vancouver, British Columbia, Canada) reveals in her most recent annual Trend Watch Report, titled The Only Way Is Up and Up, which is focused on opportunities in food upcycling. “It's not a problem that one company can solve or one country can solve,” she says. “It is something that we all have to solve together.” Couvelier agrees that figures like these and words like “food waste” can cause feelings of dread. That's one reason why she's seeking to discuss the issue from an upcycling mindset. “This isn't meant to scare people — this is a rallying cry for collaboration,” she adds. Source: Christine Couvelier, Global Culinary Trendologist, Culinary Concierge, Vancouver, British Columbia, Canada. Phone (250) 589-5845. Email: [email protected]. Website: www.culinaryconcierge.ca
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.271 | 0.117 |
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".