A systematic review of food losses and food waste generation in developed countries
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 objective of this systematic literature review was to compile and assess food losses and waste estimates, from developed countries, across the food supply chain. The methodology involved systematically identifying studies and extracting, compiling and analysing their estimates of food losses and waste. Of the 55 estimates extracted, from these studies, the most (43·6%) were from the consumption (average 114·3 (kg/capita)/year) part of the food supply chain. On average, total food losses and waste were 198·9 (kg/capita)/year. While this review revealed a high degree of variability of estimates and inconsistent trends for the independent variables: scope of food waste, geography and study methodologies; food waste generation, at the consumption part of the food supply chain, was significantly higher for North American compared with European estimates (p = 0·003); and significantly higher (p = 0·030) for indirect than direct estimates. Similarly, total food waste generation indirect estimates were significantly higher (p = 0·035) than directly measured estimates. To improve the accuracy and precision of food losses and waste estimates, additional research is required to develop and implement a bespoke, weight-based and statistically sound methodology for its direct measurement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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