Institutional food waste and the circular economy: Is it time to revisit produce waste in global food supply chains?
Bibliographic record
Abstract
Food waste generated by large systems including hospitals and postsecondary institutions can greatly influence the reduction, reuse, recycling, and recovery of produce and other perishable waste items that are essential to human health and nutrition. We position the issue of food waste as it pertains to the circular economy to support the provision of fruits and vegetables through networks of food donating charitable organizations such as food banks in Canada. Similar models can be replicated in other settings where either government or private citizens can work with institutional partners to divert food susceptible to loss or waste to promote rescue. Added benefits include climate change reduction and support for improved planetary health. Wide-scale thinking is needed about these issues given the pertinence of global warming and climate change, and the need to sustain improved nutrition for our growing populations impacted by chronic diseases across the lifespan. Further study is needed to estimate the true quality and quantity (volume) of waste and benefits associated with diversion to human consumption related purposes. • Institutional food waste remains an unexamined avenue to re-divert raw produce. • Systematic accounting of the volume and quality of waste generated can help re-diversion. • Food rescue may enable charitable aid utilizing communities to benefit from re-diversion. • Circular economy driven re-diversion can impact food security. • Climate bottom-line assessments for large institutional food service systems are warranted.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.019 | 0.033 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".