Pesticide Residues on Fruits and Vegetables from Ontario, Canada, 1991–1995
Why this work is in the frame
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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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.007 | 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 it