Using Biomonitoring Data from the Canadian Health Measures Survey and Biomonitoring Equivalents to Assess Risk Associated with Essential Nutrients
Bibliographic record
Abstract
Health Canada conducts human health risk assessments for priority substances under the Chemicals Management Plan. Many of these are essential nutrients for human health, including selenium, molybdenum, iodine and zinc. However, elevated exposures can result in adverse health effects. Although the diet is the primary route of exposure to these nutrients, they are ubiquitous in environmental media and as such, there is also potential for exposure from air, water, soil, and house dust. In addition, these substances are present in thousands of products available to consumers; which contributes to overall exposure.With the availability of human biomonitoring data from the Canadian Health Measures Survey (CHMS), Health Canada was able to evaluate integrated exposure from all sources, as biomonitoring data can be used as a measure of internal exposure regardless of the source. General population biomonitoring data from the CHMS, coupled with biomonitoring equivalents (BEs), i.e., the blood or urine equivalent of an exposure guideline such as a tolerable daily intake or reference dose, provided evidence of safety for the general population for selenium, molybdenum, iodine and zinc. Comparison of the CHMS biomonitoring data with data from smaller target studies revealed subpopulations in Canada with elevated exposure and the potential for adverse health effects.Using the same approach, biomonitoring data coupled with BEs for nutritional adequacy (e.g., the estimated average requirement) can also be used to evaluate the nutritional status of Canadians. On average, Canadians meet dietary recommendations for these nutrients, although some deficiencies have been observed across the population.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".