Improving Post-Harvest Handling to Minimise Fruit and Vegetable Losses in Côte d'Ivoire: An Agricultural Perspective
Bibliographic record
Abstract
The post-harvest handling of fruits and vegetables in Côte d'Ivoire is often inefficient, leading to significant losses. Agricultural field surveys were conducted with local farmers and extension workers to gather data on current handling methods. A statistical model was developed to predict the impact of improved handling practices on loss reduction. Field studies showed that a structured cooling system reduced losses by approximately 20% compared to traditional storage methods, indicating significant potential for loss minimization. Improved post-harvest handling can significantly reduce fruit and vegetable losses in Côte d'Ivoire, with the most effective strategy being the implementation of controlled temperature storage systems. Local authorities should promote the adoption of controlled cooling systems to farmers through training programmes and subsidies. Farmers are advised to implement these practices to maximise yield and quality. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".