Biomonitoring of herbicides in Ontario farm applicators.
Bibliographic record
Abstract
OBJECTIVES: Biomonitoring of pesticide residues in urine offers the advantages of integrating exposure due to all routes of entry and accounting for individual differences in several factors such as pharmacokinetics. The study was designed to measure the body burden of 2,4-dichlorophenoxyacetic acid (2,4-D) and 4-chloro-2-methylphenoxyacetic acid (MCPA) in farm applicators and to measure compliance with label recommendations regarding the use of personal protective gear and the impact of such use on exposure. METHODS: Farmers (N=126) from Ontario, Canada, collected a preexposure spot sample of urine and then two consecutive 24-hour urine samples immediately following the farmers' first use of these herbicides during 1996. Details on the pesticides used and handling practices were collected by questionnaire. RESULTS: For the farmers who reported using 2,4-D, the mean urinary concentration was 27.6 microg/l in the day-1 sample and 40.8 microg/l in the day-2 sample. The comparable figures for MCPA were 44.4 microg/l and 58.0 microg/l, respectively. Adherence to all of the recommended personal protective gear was rare (3%). Wearing goggles or a face shield during mixing and loading was associated with the lowest exposures. CONCLUSIONS: The urinary concentrations of 2,4-D and MCPA of these farm applicators were of the same order of magnitude as those published in the past decade, but lower than earlier studies, indicating that improvements in education, equipment, and labeling have likely had an impact on the degree of exposure in occupational settings.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".