Development of an Agricultural Job-Exposure Matrix for British Columbia, Canada
Bibliographic record
Abstract
Farmers in British Columbia (BC), Canada have been shown to have unexplained elevated proportional mortality rates for several cancers. Because agricultural exposures have never been documented systematically in BC, a quantitative agricultural Job-exposure matrix (JEM) was developed containing exposure assessments from 1950 to 1998. This JEM was developed to document historical exposures and to facilitate future epidemiological studies. Available information regarding BC farming practices was compiled and checklists of potential exposures were produced for each crop. Exposures identified included chemical, biological, and physical agents. Interviews with farmers and agricultural experts were conducted using the checklists as a starting point. This allowed the creation of an initial or 'potential' JEM based on three axes: exposure agent, 'type of work' and time. The 'type of work' axis was determined by combining several variables: region, crop, job title and task. This allowed for a complete description of exposures. Exposure assessments were made quantitatively, where data allowed, or by a dichotomous variable (exposed/unexposed). Quantitative calculations were divided into re-entry and application scenarios. 'Re-entry' exposures were quantified using a standard exposure model with some modification while application exposure estimates were derived using data from the North American Pesticide Handlers Exposure Database (PHED). As expected, exposures differed between crops and job titles both quantitatively and qualitatively. Of the 290 agents included in the exposure axis; 180 were pesticides. Over 3000 estimates of exposure were conducted; 50% of these were quantitative. Each quantitative estimate was at the daily absorbed dose level. Exposure estimates were then rated as high, medium, or low based on comparing them with their respective oral chemical reference dose (RfD) or Acceptable Daily Intake (ADI). This data was mainly obtained from the US Environmental Protection Agency (EPA) Integrated Risk Information System database. Of the quantitative estimates, 74% were rated as low (< 100%) and only 10% were rated as high (>500%). The JEM resulting from this study fills a void concerning exposures for BC farmers and farm workers. While only limited validation of assessments were possible, this JEM can serve as a benchmark for future studies. Preliminary analysis at the BC Cancer Agency (BCCA) using the JEM with prostate cancer records from a large cancer and occupation study/survey has already shown promising results. Development of this JEM provides a useful model for developing historical quantitative exposure estimates where is very little documented information available.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".