Food Losses and Waste in China and Their Implication for Water and Land
Bibliographic record
Abstract
Conventional approaches to food security are questionable due to their emphasis on food production and corresponding neglect of the huge amount of food losses and waste. We provide a comprehensive review on available information concerning China's food losses and waste. The results show that the food loss rate (FLR) of grains in the entire supply chain is 19.0% ± 5.8% in China, with the consumer segment having the single largest portion of food waste of 7.3% ± 4.8%. The total water footprint (WF) related to food losses and waste in China in 2010 was estimated to be 135 ± 60 billion m(3), equivalent to the WF of Canada. Such losses also imply that 26 ± 11 million hectares of land were used in vain, equivalent to the total arable land of Mexico. There is an urgent need for dialogue between actors in the supply chain, from farmer to the consumer, on strategies to reduce the high rates of food losses and waste and thereby make a more worthwhile use of scarce natural resources.
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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 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".