Environmental Impact of Food, Fruit and Vegetable Waste during COVID-19 Pandemic: A Review
Bibliographic record
Abstract
Apart from the major health impact, Coronavirus Disease-2019 (COVID-19) has impacted almost all sectors across the world. One of them is food, Fruit and Vegetable Markets (FVM). Lockdown implementation had different impacts in different countries, like Canada and the United Kingdom (UK) where they have logistics and supply chain of food, fruits and vegetable items and noted a shift in supply from food service to the retail channel, although the fresh food supply remains unaffected. A similar trend was seen in the metro cities of India, where online shopping has increased. In the food supply sector, both retailers and farmers had to face difficulty in storing, transporting, and selling of the goods and had to bear losses due to increased wastage. Although with an increased demand, organic farming has increased but still increased expenditure, less yield, and selling of the products are the major challenges in front of them. Food, fruit and vegetable wastes have considerably reduced at the food supply due to the obvious impact of lockdown on food supplies, however, a shortage of cold storages and supply chain at the farmer level in developing countries has resulted in more wastage. Developed countries reported increased illegal dumping of wastes in the rural areas and the stoppage of the recycling services due to the lockdown. Also, a shift in the habits of the consumer due to health and food-related issues has been seen throughout the world resulting in reduced waste generation at the consumer level. Despite all this, agricultural producer and the retail industry appears to be best placed to weather the storm. The major challenges related to the industry are sustainability in the food chain and maintaining smooth logistics and necessary precautionary measures in the event of health crises in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".