Enhancing a Job Exposure Matrix for Sun Exposure in Outdoor Workers Using Satellite Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: A typical process for producing job exposure matrices (JEM) uses measured exposure data to inform estimates of exposure by job and/or industry. For solar ultraviolet radiation (UV), quantitative data are not available, so modified approaches must be used. Methods: Via the CAREX Canada project, we used an Australian skin cancer prevention workbook to identify high-exposed jobs (spending >75% of the workday outside). For other exposure categories, we used career-selection websites that describe tasks by job and include information on outdoor work (and amount). This allowed the creation of a low-exposed category, as well as 2 moderate categories. We applied estimated proportions of workers exposed by categories to 2006 Canadian census data to obtain estimates of people exposed by industry, occupation, and sex. To enhance the JEM, we applied weightings using NASA Total Ozone Mapping Spectrometer (TOMS) satellite data to account for variation in maximum available UV in July, as well as mean UV over the summer by location. Results: Approximately 1.5 million Canadians are exposed to solar UV at work (1 in 10 workers), 83% of these are male. Major industries include construction (343,000 exposed) and farming (264,000). Variations by province occur both because of industry breakdown, but also because of available UV as estimated by satellite data. Conclusion: JEMs are a convenient way to assess exposure to carcinogens at a population level (where exposure cannot be assessed individually), and using a novel combination of workbooks, career selection websites, and satellite data has created a more powerful JEM for use in epidemiologic studies. This JEM is now being used to assign exposure in several population-based cancer studies.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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 it