Advances in Postharvest Disinfestation of Fruits and Vegetables Using Hot Water Treatment Technology-Updates from Africa
Bibliographic record
Abstract
Hot Water Treatment (HWT) provides adequate phytosanitary assurance that treated fruits and vegetables exported abroad are free from devastating quarantine pests. Two systems for HWT are currently available for commercial use namely the batch/jacuzzi and the continuous flow system depending on user requirements. Several protocols have been developed the world over and a few in Africa, but adoption has been lagging because of various factors chief among them lack of large scale validations of experiments to guide application at the commercial level. Mango, Bell pepper, avocado, and French beans play an important role in the livelihoods of people in Africa. However, their export is constrained by pests such as the invasive Oriental fruit fly, the false codling moth, and thrips. To circumvent this issue, disinfestation HWT protocols have been developed which seek to provide quarantine assurance to lucrative export markets. Hot Water Treatment technology has several advantages over other conventional phytosanitary treatments. It provides a triple function of cleaning, disinfesting, and disinfecting and is friendly to users, consumers of the treated commodities, and the environment. We discuss HWT in the context of its future and applicability in Africa. It is the future of postharvest treatments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".