Surveillance of Environmental Pesticide Exposure in Canada
Bibliographic record
Abstract
ISEE-0631 Background/Objective: Some of the most extensively used pesticides in Canada’s agricultural industry are classified as “possibly” carcinogenic by the International Agency for Research on Cancer (IARC). However, information on population exposure to these pesticides is extremely limited. The objective of our study is to apply a new pesticide exposure assessment technique to conduct Canada-wide surveillance of environmental pesticide exposure. Methods: A province-specific crop exposure matrix is being developed for Canada to identify uses of 14 pesticides (IARC “possible” carcinogens) to provide estimates on the prevalence of use. This information is used to classify the probability of environmental exposure to a specific pesticide as being ‘high’, ‘moderate’ or ‘low’. Geographic information systems (GIS) and Statistics Canada boundary data are used to identify high agricultural activity areas within sub-provincial regions. Using GIS and National Census of Population data, the population currently residing within each agricultural activity area is determined. Results: As an example, results for environmental exposure to phenoxy herbicide 2,4-D in Ontario are presented. Ontario has 46 high agricultural activity areas, with estimated prevalence of 2,4-D use ranging from 38–99%. Agricultural areas were classified into three probability of exposure groups based on the tertile distribution of the prevalence of 2,4-D use estimates (‘high‘ exposure area if >90% use; ‘moderate’ exposure area if 70–90% use; ‘low‘ exposure area if < 70% use). Approximately 3.1 million people live in Ontario agricultural areas and are at risk of 2,4-D exposure. Population estimates by agricultural area exposure group are approximately: 292,000 (9.5%) people in ‘high’ exposure areas, 984,000 (32%) people in the ‘moderate‘ exposure areas, and 1.8 million (58.5%) people in ‘low’ exposure areas. Conclusion: This surveillance information will be used to geographically identify high risk pesticide exposure populations; it will also have an essential role in hazard surveillance, risk assessment and epidemiologic research.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".