Circular, Resilient, and Traceable: A Framework for the Future of Agri-Food Supply Chains
Bibliographic record
Abstract
Food waste in agri-food supply chains (AFSCs) often stems from fragmented stakeholder collaboration and unclear roles in promoting food circularity. This study develops a novel framework categorizing stakeholders into three groups: upstream actors (e.g., farmers, processors), connectors (e.g., logistics providers), and downstream actors (e.g., retailers, consumers) to address the complex interplay of materials and actors in AFSCs. Unlike prior research focusing primarily on biodegradable materials, this study adopts a holistic approach to circularity by examining the entire material lifecycle. Drawing on the Ellen MacArthur Foundation's "butterfly diagram," this study integrates biological and technical cycles, emphasizing reuse, refurbishing, remanufacturing, and recycling of non-biodegradable materials alongside natural biodegradation processes. Not only does the framework connect material flows with stakeholder roles, but it also highlights how traceability enhances material tracking and waste reduction, while resilience ensures a sustainable and uninterrupted food supply. These insights offer actionable pathways for achieving circularity in AFSCs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".