Screening of Population Level Biomonitoring Data from the Canadian Health Measures Survey in a Risk Based Context
Bibliographic record
Abstract
Canada’s comprehensive and nationally-representative Canadian Health Measures Survey includes a biomonitoring component which has measured over 250 chemicals in approximately 29,000 Canadians over a ten-year period. Our capacity to interpret biomonitoring results in relation to the risks these levels pose to human health is gradually improving with the development of biomonitoring equivalents (BE) and human biomonitoring values (HBM values). Biomonitoring data from various cycles of the CHMS are compared with published BE values for chemicals with short half-lives, persistent chemicals, and volatile organic compounds (VOCs). Hazard quotients are calculated as the ratio of the biomarker concentration to the chemical-specific BE value using both the geometric mean (GM) and upper bound (95th percentile). Hazard quotients near or exceeding a value of 1 are indicative that exposure levels are near or exceeding the exposure guidance values on which BEs are based. For example, acrylamide is assessed by comparing levels of two of its metabolites in blood, namely AAVal and GAVal. Hazard quotients for AAVal exceed the BE at the 95th percentile for smokers. This exceedance is not seen in non-smokers. A similar pattern is observed for GAVal. Although more work will be needed to fully evaluate available Canadian human biomonitoring data against established guidance values, this screening exercise can help to prioritize risk management actions for some toxic chemicals and show the importance of continued biomonitoring to assess population exposures and exceedances.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".