The Risk of Dietary Exposure to Pesticide Residues and Its Association with Pesticide Application Practices among Vegetable Farmers in Arusha, Tanzania
Bibliographic record
Abstract
This study was conducted to assess dietary exposure to pesticide residues and pesticide application practices leading to the presence of these residues among vegetable farmers in Arusha, Tanzania. Face-to-face interviews using semi-structured questionnaires (including 24-hour recall and food frequency questionnaire techniques) were conducted to collect information on pesticide application practices and vegetable consumption, from 76 farmers. A sample of ready-to-eat vegetables was collected from each farmer's household to determine the level of pesticide residues. Pesticide residues were analyzed by gas chromatography-mass spectroscopyA deterministic approach was used to assess dietary exposure to pesticide residues. Among the analyzed samples, 31.4% contained detectable levels of organophosphate residues. The detected organophosphates were dimethoate (mean, 8.56 mg kg-1), acephate (mean, 2.9 mg kg-1), profenofos (mean, 8.44 mg kg-1), dichlorvos (mean, 20.8 mg kg-1) and malathion (mean, 5.47 mg kg-1). The mean exposure for dimethoate (0.0021 mg kg-1 body weight (wt) day‑1 was higher than its corresponding acceptable daily intakes of 0.002 mg kg-1bwd‑1 resulting in hazard quotient of 1.044 with a consequent hazard index of 1.19 for organophosphates. Pyrethroid pesticides (permethrin, cypermethrin, and lambda-cyhalothrin) were also detected but at a lower frequency (17.1%) and hazard index (0.029). The exposure to pesticide residues was significantly associated with limited access to expert advice on pesticide application (p=0.031, adjusted odds ratio=6.56) and over-dosage (p=0.038, adjusted odds ratio=3.751).The risk may be minimized by increasing access to support by extension service providing guidance on good practices and ensuring application of appropriate doses for 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".