Cadmium and Mercury Exposure among Dene/Métis Communities of the Northwest Territories, Canada
Bibliographic record
Abstract
In northern Indigenous communities, eating traditional foods (which include locally harvested wild game, fish, game birds, and plants) is associated with improved nutritional status. However, traditional foods can occasionally increase human exposures to contaminants. To mitigate potential risks from elevated contaminant levels in fish and wildlife, public health officials regularly respond to the results from environmental monitoring programs by designing notices that advise individuals to limit their consumption of particular traditional foods. For example, elevated mercury (Hg) levels in Walleye, Northern Pike, and Lake Trout in some subarctic lakes led to the release of a series of consumption notices by the Government of the Northwest Territories Department of Health and Social Services. Also, high levels of cadmium (Cd) in the organs of moose from the Southern Mackenzie Mountains, Canada resulted in consumption notices recommending people to limit their consumption of kidney and liver of moose harvested from this region.Since 2016, a community-based human biomonitoring project has been run in nine Dene/Métis communities of the Dehcho and Sahtú regions of the Northwest Territories Mackenzie Valley (n=538). This project included dietary assessments (e.g., 24-hour Recall, Food Frequency Questionnaire) as well as hair, urine and blood sampling to characterize contaminant exposures among participants. Although Hg and Cd levels in traditional foods from the Northwest Territories are occasionally elevated, the results from this biomonitoring research show exposures to these metals to be generally similar to those observed in other populations in Canada. These results are supporting ongoing efforts at the community and territorial level to design follow up plans in response to environmental monitoring data.
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.
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 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".