Applying the multiple-barrier approach for microbial risk reduction in the post-harvest sector of wastewater irrigated vegetables
Bibliographic record
Abstract
Post-harvest interventions are an important component of a multiple-barrier approach for health-risk reduction of wastewater-irrigated crops as recommended by the 2006 edition of the WHO Guidelines for safe wastewater irrigation. This approach draws on principles of other risk-management approaches, in particular the hazard analysis and critical control point (HACCP) concept. Post-harvest measures are of particular importance as they can address possible on-farm precontamination, and also contamination that may occur after the crops leave the farm. Key factors influencing microbial contamination along the farm to fork pathway are basic hygiene and temperature management. Both factors are, however, hardly under control in most developing countries where microbial contamination and proliferation are supported by low education, limited risk awareness, rudimentary technical infrastructure and unenforced regulations. In the face of these challenges, the most successful strategies to enhance food safety will involve interventions at multiple control points along the production chain, with emphasis on local safety targets and innovative educational programmes fitting local knowledge, culture and risk perceptions. The WHO (2006) recommended health-based targets for risk reduction in wastewater irrigation provide the required flexibility for risk mitigation in line with the concept of food-safety objectives (FSO).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".