Survey of Pesticides Residue Levels in Fresh Fruits and Vegetables across Southern Jordanian Wholesale Markets
Bibliographic record
Abstract
HistoryThe use of pesticides enhances crop productivity and quality by controlling insect pests.Still, their excessive use negatively affects the environment, human health, groundwater quality, and reduces biodiversity.Pesticides, including imidacloprid can persist in the environment for a long time, affecting air, water, and soil, consequently negatively impacting human health.This study evaluated imidacloprid residues in 390 samples of 13 types of fruits and vegetables, sourced from 30 prominent wholesalers across southern Jordanian wholesale markets.Imidacloprid was selected due to its frequent use as a systemic insecticide in agricultural production, which increases the risks associated with residue accumulation.Residues were detected in 77.7% of samples, with concentrations ranging from below the detection threshold to 1.30mg.kg-1 .The highest mean concentrations were observed in eggplant, apple, cauliflower, and cabbage (0.45, 0.41, 0.35, 0.30mg.kg-1 , respectively), while apricots, potatoes, and grapes had the lowest concentrations.Imidacloprid was not detected in 32.3% of samples.Overall, 14.4% of samples exceeded the maximum residue limit (MRL) set by Codex, and 5.9% exceeded the Canadian PMRA standards.Furthermore, the results showed that eggplant and apple samples recorded for pesticide residues significantly exceeded Codex and PMRA MRLs.Despite some samples exceeding MRLs, the hazard index (HI) values for all samples were below unity (<1), indicating low immediate risk to consumer's health.These findings underscore the need for enhanced regulatory measures to mitigate potential health risks posed by pesticide residues in fresh produce.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".