Traditional Food Consumption and Other Determinants of Exposure for Lead, Cobalt, Manganese, and Hexachlorobenzene in Northern Canada
Bibliographic record
Abstract
Results of a 2019 human biomonitoring study indicated that several parameters, including lead, cobalt, manganese, and hexachlorobenzene, were elevated in blood and urine samples in Old Crow, Yukon, in comparison to the general Canadian population. This study aims to identify possible local determinants of levels of these parameters, including consumption of locally harvested traditional foods, lifestyle factors, and demographics, in Old Crow and, for comparison, two other northern populations: communities in the Dehcho and Sahtú regions of the Northwest Territories. We ran generalized linear models to identify possible associations between individual determinants of exposure and key biomarkers, controlling for age and sex. In Old Crow, several variables were associated with elevated exposure levels of these biomarkers, including drinking untreated river water (29% higher blood manganese levels and 120% higher blood lead levels), eating caribou kidneys (22% higher blood manganese levels and 58% higher blood lead levels), and eating whitefish (28% higher blood cobalt levels). Additionally, in order to differentiate results in Old Crow from those in other northern regions and to identify trends across regions, we observed relationships between consumption of moose and caribou organs and lead and hexachlorobenzene levels in the reference populations and pooled population groups. Though levels of particular contaminants may be elevated in some traditional foods, these foods remain an important source of nutrients for members in these communities and provide other benefits, including increased physical activity through harvesting, mental health improvements, and spiritual wellness.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".