<i>N</i>-methyl carbamate concentrations and dietary intake estimates for apple and grape juices available on the retail market in Canada
Bibliographic record
Abstract
Infants and young children consume fruit juices and drinks at rates exceeding those of older children and adults. Carbamate pesticides are known to be used on a broad spectrum of crops, including orchard and vine crops such as apples and grapes. Concern over potential exposure to these acutely toxic pesticides by infants and young children has increased in the last decade. Liquid chromatography with fluorescence detection was used to determine the concentrations of seven N-methyl carbamates and three transformation products in domestic and imported apple and grape juices collected across Canada. Carbaryl was the most frequently (58.6%) detected N-methyl carbamate in juice samples studied. It was observed more frequently in grape juices than in apple or mixed juices. Oxamyl and methomyl were detected in apple juice samples, although they were below detection limits in all grape and mixed juice samples analysed. Maximum levels of carbaryl, methomyl and oxamyl were 93, 6.7 and 4.6 ng ml(-1), respectively. All other analytes were not present in any juice sample at concentrations above the method detection limit (0.3 ng ml(-1)). In all cases, N-methyl carbamate residues were well below the maximum residue limit established for apples and grapes in the Canadian Food and Drug Regulations. No estimated dietary intakes were above the acceptable daily intakes in any age-sex category, where an acceptable daily intake has been proposed. Carbaryl short-term intake estimates were calculated and all were below the proposed acute reference doses.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".