Pesticide Residues on Fruits and Vegetables from Ontario, Canada, 1991–1995
Bibliographic record
Abstract
For the 5-year period 1991 to 1995, 1536 vegetable and 802 fruit samples were analyzed. The purpose of this study was to determine if pesticides were present on Ontario-produced fruits and vegetables, and if so, to determine if residues violated maximum residue limits (MRLs). Overall, 31.5% of the samples had no detectable pesticide residues, whereas 68.5% contained one or more residues. Most of the residues were present at very low concentrations; 48% of the detections were < 0.1 parts per million (ppm), and 86% were < 1 ppm. However, violations of MRL were observed in only 3.2% of the vegetables samples and 3.1% of the fruit samples. In addition, 4.8% of the samples contained a "technical" violation, that is, there was no specified MRL for the pesticide-commodity combination and the residues exceeded 0.1 ppm. Of the detectable residues, 63% were < 10% of the MRL, whereas 89% were < 50% of the MRL. More fruit samples (91.4%) had a detectable residue, compared with vegetable samples (56.6%). Fruit is often treated close to harvest or post harvest to ensure that wholesome produce reaches the consumer. Forty-six percent of the samples contained 2 or more residues, and 2% of all samples had more than 5 different pesticides detected; fruit samples tended to have more multiple residues. The most frequently found pesticides were captan, the dithiocarbamate fungicides, endosulfan, azinphos-methyl, phosmet, parathion, and iprodione. These pesticides were also used in the greatest quantity for crop production. Overall, the data agree fairly closely with those reported for the U.S. Department of Agriculture Pesticide Data Program because the 2 programs have similar analytical goals and objectives.
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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 | 0.003 |
| Science and technology studies | 0.002 | 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".