Pesticide residues in fresh fruits imported into the United Arab Emirates
Bibliographic record
Abstract
Pesticides are a major public health issue connected with excessive use because they negatively impact health and the environment. Pesticide toxicity has been connected to various human illnesses by means of pesticide exposure in direct or indirect ways. A total of 4513 samples of imported fresh fruits were collected from Dubai ports between 2018 to 2020. Their contamination by pesticides was evaluated using gas chromatography combined with mass spectrometry (GC-MS/MS) and liquid chromatography-mass spectrometry (LC-MS/MS). The display of monitoring results was based on the Maximum Residue Limit (MRL) standard as per the procedures of the European Union. Eighty-one different pesticide residues were detected in the tested fruit samples. In 73.2% of the samples, the pesticide levels were ≥ MRL, while 26.8% were > MRL standards. Chlorpyrifos, carbendazim, cypermethrin, and azoxystrobin were the most frequently detected pesticides in more than 150 samples. Longan (81.4%) and rambutan (66.7%) showed the highest number of imported samples with multiple pesticide residues > MRL. These results highlight the need to continuously monitor pesticide residues in fruits, particularly samples imported into the United Arab Emirates (UAE). Fruit samples with residues > MRL are considered unfit for consumption and prevented from entering commerce in the UAE.
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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 | 0.001 | 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".