A pre and post analysis of food and carbon flows of a surplus food café initiative
Bibliographic record
Abstract
Food waste is a systemic issue augmented by the retail and eatery sectors of the food supply chain. Surplus food redistribution can alleviate the volume of food sent to landfill or compost, and simultaneously offset greenhouse gas (GHG) emissions associated with food waste. The Rediscovery Centre Food Rescue Café is a pilot programme trialling a surplus food business model, with this research comparing incoming food, residual food waste, and GHG flows before and during the initiative. There was no significant difference in residual food waste produced, and there were GHG savings associated with surplus food use. This finding implies that incoming surplus food does not affect café waste quantity, although the café appears to utilise edible waste more efficiently than previously. The GHG savings from consuming surplus food was estimated at −13.51 kgCO2e/kg, and this will only increase as the café operates with greater quantities and food types of surplus food. Furthermore, surplus eateries like the Food Rescue Café can be an effective leverage point to create a more socially inclusive circular bioeconomy in Ireland through connecting people and communities with nutritional surplus food. Therefore, a surplus food model appears environmentally and socially sustainable.
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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 | 0.000 | 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".