Pesticide exposures in professional turf applicators, job titles, and tasks performed: Implications of exposure measurement error for epidemiologic study design and interpretation of results
Bibliographic record
Abstract
BACKGROUND: Little information on the validity of job title and task classifications, for the prediction of pesticide use or exposure, is available. METHODS: Job titles and task classifications were evaluated in relation to the absorbed dose of herbicides in 98 professional turf applicators. Self-reported use over a 1-week period and other proxies of pesticide use were compared with employer records. RESULTS: Job titles and tasks performed explained (R(2)) 11% and 16% of the variation in dose, respectively. Individuals who sprayed pesticides only, had the highest average doses in the study followed by those spraying and mixing, and those mixing only. The use of 2,4-D products by individual workers over a work season was not related to standardized measures of the amount purchased or used at the company. CONCLUSIONS: These findings suggest that job titles and tasks performed are poor proxies of pesticide use and exposures in professional turf applicators.
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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 | 0.115 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".