Screening of population level biomonitoring data from the Canadian Health Measures Survey in a risk-based context
Bibliographic record
Abstract
Since 2007, the Canadian Health Measures Survey (CHMS) has been collecting biomonitoring data from the general Canadian population and has provided, to date, nationally representative concentrations for hundreds of environmental biomarkers in blood or urine. Biomonitoring Equivalents (BEs) have been developed as tools to help interpret biomonitoring data in a health risk context at a population level. In this paper, BEs are used to relate biomonitoring data from the CHMS (2007-2011) to existing exposure guidance values developed by Health Canada and other government agencies. Chemical-specific hazard quotients (HQs) and/or cancer risk estimates are calculated using existing BEs corresponding to environmental chemicals analyzed in the CHMS. For the majority of environmental chemicals, calculated HQ values are less than 1 indicating exposure is below published exposure guidance values. Individual biomonitoring data for two biomarkers of metal exposure (inorganic arsenic and cadmium) resulted in HQ values exceeding 1 suggesting that exposure may be above existing guidance values for a portion of the population, at least intermittently. This type of analysis may be used by researchers, risk assessors, and risk managers in prioritization efforts.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".