Assessing and addressing food waste in university dining
Bibliographic record
Abstract
In Canada, approximately 35.5 million metric tonnes of food are wasted annually, with avoidable food loss and waste costing Canada $49.5 billion (Nikkel et al., 2019). The volume of food waste in Canada harms environmental sustainability and leads to economic inefficiency and social inequality (Soma, 2022). This study at Brescia University College explored methods to reduce plate waste in a university restaurant. Using two research designs, this study first analyzed and categorized all plate waste, finding non-food waste predominantly during breakfast (59.8 percent) and lunch (54.3 percent). Edible waste was highest during dinner (51.0 percent); carbohydrates contributed to the most waste (54.5 percent) and plant-based protein (3.1 percent) the least. The second design involved student participation in waste weighing and completing questionnaires. Results showed a median edible waste of 19.0g, with fullness, poor taste, large portion size, and inability to bring home leftovers as the main reasons for waste. Meal plan students had a significantly higher amounts of plate waste than non-meal plan students (p<0.001). Recommendations include serving smaller portions, improving food taste, and offering storage solutions for leftovers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".