Analysis of Health Risk Factors in the Vegetable Production Chain in the City of N'Djamena-Chad
Bibliographic record
Abstract
Several market gardeners have settled in the city and supply urban markets with fresh vegetables throughout the year. Despite their nutritional importance, market gardening products may carry health risks. The objective of this study is to identify and analyse the potential risk factors that could lead to the appearance of microbiological and physicochemical hazards in the production chain of fresh vegetables from these market gardening operations. The work was carried out in 5 permanent market gardening sites in the city of N'Djamena (Chad, Africa) and involved 96 market gardeners surveyed. Data related to production methods were collected. Standard methods were used to carry out microbiological analysis tests on 15 samples of vegetables and fruits taken from 3 sites.The results of the survey show that urban market gardening in N'Djamena is dominated by two plant species: lettuce (Lactuca sativa) and rocket (Eruca sativa). It is geared towards the production of leafy vegetables. The health risks associated with the conditions of production are numerous and real: the proximity of roads, the use of dirty water for irrigation, the overdose of chemical fertilizers (urea) and pesticides, and finally the unhygienic harvesting and transport. The high-water content of fresh vegetables and the lack of processes for the elimination of pathogenic microorganisms also do not guarantee the sanitary quality of the vegetables produced and can thus increase the risk of foodborne infections. The results of the microbiological evaluation showed the presence of germs pathogens including Escherichia coli, Staphylococcus aureus, Aeromonas spp. and Salmonella sp. in vegetable and fruit. Therefore, the best strategy to obtain a healthy product is to educate producers on good agricultural practices including reasoned fertilization, clean water, treated wastewater, approved pesticides.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".