On the Potential of Packaging for Reducing Fruit and Vegetable Losses in Sub-Saharan Africa
Bibliographic record
Abstract
Access to food remains a critical issue in Sub-Saharan Africa. In fact, 24.1% of its population suffers from undernourishment, and malnutrition affects more than a third of children under five years old. This problem will be exacerbated as the Sub-Saharan African population is predicted to double by 2050. To address this problem, it is imperative to meaningfully improve accessibility of fruits and vegetables for the population.They are an excellent source of vitamins and minerals that can fight malnutrition. Fruit and vegetable accessibility can be improved by reducing losses, which are estimated on average to be 50%. A literature review shows that there are many areas where solutions can be implemented to reduce these losses. These areas, in order of decreasing occurrence in the literature are: Cold storage, harvesting methods and pre-storage treatments, packaging, transport to markets and the sale stage. The reduction of food waste in SSA involves the establishment of better practices in all these areas. After analysis, it emerges that packaging should generate more interest due to its comparative ease of implementation to support other technologies like cold storage. Packaging made from agricultural waste or non-consumable materials should be highlighted to prevent pollution issues. This solution, in addition to offering a strong potential to fight against pollution, could also increase farmers' income.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".