Levels of Contaminants in Human Populations in the Canadian Arctic
Bibliographic record
Abstract
Levels of environmental contaminants, which are transported to the Arctic via air/water currents, have been shown to be elevated in northern populations, primarily Inuit. For more than 25 years, the Northern Contaminants Program (NCP) has funded research in response to concerns about exposure to contaminants from traditional, northern indigenous diets. Recently, NCP completed a human health assessment report, which provides a summary of current knowledge regarding contaminants and human health in northern Canada.Data from NCP funded studies were compiled to summarize levels of contaminants among Inuit children, pregnant mothers, women of childbearing age, and adult men and women. Data was also used to identify time trends and provide regional descriptive comparisons.This assessment found that concentrations of contaminants are higher in Inuit from northern Canada relative to the Canadian general population. This is primarily due to dietary exposure to contaminants from the consumption of certain country foods such as marine mammals. Inuit populations living in Nunavut and Nunavik, where these country foods are typically eaten more frequently or in larger amounts, had higher levels of contaminants. Time trend data show declining levels of organochlorines and metals (such as mercury and lead), by up to 80% and 60% respectively, in pregnant Inuit women from Nunavik (1992-2013). Despite this, levels remain elevated compared to the Canadian general population. Since the completion of the assessment new projects have been funded and these will also be highlighted.Strong time trend data are available for Nunavik; however more information is needed in other regions. New chemical groups have emerged in the Arctic. While human exposure data is still limited, data is being generated through new research. Additional conclusions, knowledge gaps and recommendations for future northern studies are detailed in the NCP human health assessment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".