Rethinking food loss and waste to promote sustainable resource use and climate change mitigation in agri-food systems: A review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The sustainable agri-food system is an important sector recognized for promoting the United Nations' Sustainable Development Goals on food security, resource conservation and climate change mitigation. However, the increasing food loss and waste (FLW) along the supply chains has continued to hinder these goals. This study evaluates the trend of FLW research from 1975 to 2022 and how it promotes the achievement of resource and environmental sustainability in agri-food systems. The salient research themes and hotspots that are of interest to researchers were identified. Bibliometric and network analyses were carried out on scholarly research articles from the Scopus database using bibliometrix and VOSviewer. Furthermore, the content analysis was conducted on the selected highly influential articles containing relevant data to understand the role of FLW in promoting sustainable agri-food systems. The results showed disaggregate and unbalanced research distribution on the impacts of FLW among the countries, with China and the United States having the highest contributions. The identified major research themes relating to sustainable agri-food systems are food waste and sustainable systems, food waste management and food waste impact assessment. Moreover, the circular economy was found to be a relatively new approach being explored in agri-food systems to promote FLW reduction and ensure sustainability of resource use. This study highlights the critical role of the impact of FLW in addressing the grand challenge of food security, resource use efficiency and environmental sustainability.
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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 | 0.018 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it