Post-harvest Loss Assessment of Banana (Musa spp.) at Jimma Town Market
Bibliographic record
Abstract
Post-harvest loss of banana in Jimma town market was accounted a total loss of 26.5% in the supply chain. Of these, more percent of the total losses were being observed at the retail market (64.10%) and whole-salers level (35.90%). Mechanical damage followed by improper transport and improper storage were identified as the main causes of banana loss at whole-salers level while fruit rotting followed by improper ripening and mechanical damage were identified as the main causes to the loss of banana fruit at retail level. Hence, the current post-harvest management system of banana at whole-salers and retail level is inadequate. There is no sufficient attention given for the post-harvest management of banana in the supply chain. It was also observed that, there is a knowledge gap between the respondents in their experience of proper fruit handling techniques. Therefore, to reduce the level of post-harvest losses of banana, more emphasis should be given to post-harvest handling practices. The loss can be minimized or prevented by awareness creation, education and training about the importance of post-harvest losses, adopting better management operations, careful handling and packaging to the supply chain actors.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".