Seasonal variations in exposure to methylmercury and its dietary sources among pregnant Inuit women in Nunavik, Canada
Bibliographic record
Abstract
Among populations living in close connection with the sea, rivers and lakes for subsistence, diet varies according to local monthly wildlife species availability and food preferences. This may lead to variations in methylmercury (MeHg) exposure over a year, although no biomonitoring studies have documented this issue in Circumpolar populations, the most exposed to international Hg emissions. Our aim was to characterize seasonal variations in MeHg exposure among pregnant Inuit women from Nunavik and to identify country foods responsible for these variations. Between October 2016 and March 2017, 97 participants were recruited. Blood mercury (Hg) was tested and hair Hg was measured by centimeter as a surrogate for monthly MeHg exposure over the past year. Latent class growth analysis was conducted to identify groups of pregnant women with similar hair Hg monthly trajectories. Country foods consumption was documented by season. Seasonal daily intakes of MeHg were estimated based on concentrations in country foods. Retrospective monthly hair Hg analyses revealed that MeHg exposure was lowest in winter, and highest in summer and early fall months. Three latent classes (groups) of pregnant women with similar trajectories of monthly hair Hg variations were identified: high (n = 20, 21%), moderate (n = 38, 41%) and low variation (n = 35, 38%). Beluga meat was the country food contributing to most of daily MeHg intake, primarily during summer and fall, and was the only one associated with the odds of being classified into moderate and high variation groups (OR 95% CI: 1.19 [1.01-1.39] and 1.25 [1.04-1.50]). These findings underscore the importance of monthly variations in exposure to MeHg due to the seasonality of local foods consumed and responsible for elevated MeHg exposure. Further studies critically need to understand local diet fluctuations over a year to adequately assess MeHg exposure, adopt timely preventive interventions and evaluate the effectiveness of the Minamata Convention.
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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".