Levels of metals and persistent organic pollutants in traditional foods consumed by First Nations living on-reserve in Canada
Bibliographic record
Abstract
OBJECTIVES: First Nations may have a higher risk of contaminant exposure from the consumption of traditional foods. The objective of this study was to measure concentrations of metals and organochlorines in traditional foods commonly consumed by First Nations in Canada and estimate the risk from dietary exposure. METHODS: Data were collected from the participatory First Nations Food, Nutrition and Environment Study (2008-2018). Traditional food samples were collected by community members and concentrations of metals and organochlorines were measured. The population-weighted mean daily contaminant intake from traditional food items was estimated. Hazard quotients (HQs) were calculated by dividing contaminant intake with the toxicological reference values (TRVs). RESULTS: A total of 2061 food samples (different parts and organs) from 221 species were collected. The highest concentrations of cadmium were found in the kidneys of land mammals: moose kidney was the most significant contributor to intake. The meat of land mammals and birds had the highest lead concentrations and were the most significant contributors to intake. Arsenic was highest in seaweed, and prawn was the most significant contributor. Mercury and methyl mercury were highest in harp seal meat, with walleye/pickerel contributing most to intake. Harp seal meat also had the highest p,p'-DDE and PCB concentrations, and ooligan grease and salmon were the most significant contributors to intake. The percentage of adults eating traditional food who exceeded the TRVs was 1.9% for cadmium, 3.7% for lead, 13.6% for arsenic, 0.7% for mercury, and 0% for p,p'-DDE and PCBs. All median HQs, and most 95th percentile HQs, were less than 1. CONCLUSION: These results can be used as a baseline of contaminant levels and exposure in traditional foods for future monitoring programs and to support risk assessment programs.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".