Nutritional and environmental considerations of food stockpiles in Japan and USA: reducing food waste by efficient reuse through the food banks
Bibliographic record
Abstract
The food stockpiles of local self-governing bodies comprise the first urgent response to a disaster, but stockpiled food has a best-before date and will be wasted if not used.Therefore, it is necessary to devise a method for using (reusing) stockpiles efficiently.This study proposes cooperation between local self-governing bodies, food banks, and special food supermarkets in Japan and USA to improve the food quality and nutritional value of stockpiles and reduce food waste.Japan's food stockpile is estimated to be 40,015.5tons, and though there is no exact information regarding America's stockpiles, there are estimated approximately 200 active food banks and 63,000 smaller food pantries.Analysis of Japan's stockpiled food revealed a high-energy ratio of lipids and carbohydrates along with insufficient amounts of vitamins and minerals.However, in foods supermarkets, huge amounts of vegetables and fruits are now being discarded which could provide the deficient nutrients.Through cooperation of food supermarkets and food banks, it may be possible to promote the efficient reuse of the stockpiles of local self-governing bodies and improve the nutritional value of stockpiles.During the research period, Japan had 40 food banks and reutilized 1,512 tons of food waste from these food banks each year, which is only 0.1% of the food waste discarded from the entire food industry in Japan.Subsidies provided in 2010 by the Ministry of Agriculture, Forestry, and Fisheries stimulated the activity of food banks, which work under the viewpoint of food waste reduction.The researchers investigated the profile of America's food banks compared to the food bank activity in Japan to stimulate food bank activity in both countries while considering the viewpoints of waste management, environmental impact and economics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".