Using Biomonitoring Data from the Canadian Health Measures Survey and Biomonitoring Equivalents to Assess Risk Associated with Essential Nutrients
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 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 | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it