Assessment of Food Products Lost Among Households in Rwanda: A Case Study of Rural and Urban Areas
Bibliographic record
Abstract
This study investigates food loss in rural and urban households in Rwanda, focusing on areas in the Eastern and Western Provinces for rural settings and Kigali for urban ones. A stratified random sampling technique was used to select 320 households, with 160 from rural and 160 from urban areas. Data was collected through surveys and interviews, exploring household characteristics, food consumption patterns, food loss stages along the value chain, and socio-economic impacts. The analysis revealed that food loss is more prevalent in rural areas at the production, handling, and storage stages, while urban areas experience greater loss at the consumption stage. Poor storage, spoilage, and over-purchasing were identified as significant contributors to food loss. The study suggests that rural and urban households face economic challenges due to food loss, emphasizing the need for targeted interventions, including improved storage infrastructure, consumer education, and better food management practices.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".