Assessing determinants of maternal blood concentrations for persistent organic pollutants and metals in the eastern and western Canadian Arctic
Bibliographic record
Abstract
Aboriginal peoples in the Canadian Arctic are exposed to persistent organic pollutants (POPs) and metals mainly through their consumption of a traditional diet of wildlife items. Recent studies indicate that many human chemical levels have decreased in the north, likely due to a combination of reduced global chemical emissions, dietary shifts, and risk mitigation efforts by local health authorities. Body burdens for chemicals in mothers can be further offset by breastfeeding, parity, and other maternal characteristics. We have assessed the impact of several dietary and maternal covariates following a decade of awareness of the contaminant issue in northern Canada, by performing multiple stepwise linear regression analyses from blood concentrations and demographic variables for 176 mothers recruited from Nunavut and the Northwest Territories during the period 2005-2007. A significant aboriginal group effect was observed for the modeled chemicals, except for lead and cadmium, after adjusting for covariates. Further, blood concentrations for POPs and metals were significantly associated with at least one covariate of older age, fewer months spent breastfeeding, more frequent eating of traditional foods, or smoking during pregnancy. Cadmium had the highest explained variance (72.5%) from just two significant covariates (current smoking status and parity). Although Inuit participants from the Northwest Territories consumed more traditional foods in general, Inuit participants from coastal communities in Nunavut continued to demonstrate higher adjusted blood concentrations for POPs and metals examined here. While this is due in part to a higher prevalence of marine mammals in the eastern Arctic diet, it is possible that other aboriginal group effects unrelated to diet may also contribute to elevated chemical body burdens in Canadian Arctic populations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".