Prenatal exposure of the northern Québec Inuit infants to environmental contaminants.
Bibliographic record
Abstract
The Inuit population residing in Nunavik (northern Québec, Canada) relies on species from the marine food web for subsistence and is therefore exposed to high doses of environmental contaminants such as polychlorinated biphenyls and methylmercury and to a lesser extent lead. In view of the neurotoxic properties of these substances following developmental exposure, we initiated a study on infant development in this remote coastal population. Here we report the magnitude of prenatal exposure to these contaminants and to selective nutrients in Inuit mothers and their newborns who were recruited on the Hudson Bay coast. We conducted interviews during the women's pregnancies and at 1 and 11 months postpartum and collected biological samples for mercury, lead, polychlorinated biphenyls (PCBs), and chlorinated pesticides analyses as well as selenium and N-3 polyunsaturated fatty acids (n3-PUFA). Cord blood, maternal blood, and maternal hair mercury concentrations averaged 18.5 microg/L, 10.4 microg/L, and 3.7 microg/g, respectively, and are similar to those found in the Faroe Islands but lower than those documented in the Seychelles Islands and New Zealand cohorts. Concentrations of PCB congener 153 averaged 86.9, 105.3, and 131.6 microg/kg (lipids) in cord plasma, maternal plasma, and maternal milk, respectively; prenatal exposure to PCBs in the Nunavik cohort is similar to that reported in the Dutch but much lower than those in other Arctic cohorts. Levels of n3-PUFA in plasma phospholipids and selenium in blood are relatively high. The relatively low correlations observed between organochlorine and methylmercury concentrations may make it easier to identify the specific developmental deficits attributable to each toxicant. Similarly, the weak correlations noted between environmental contaminants and nutrients will facilitate the documentation of possible protective effects afforded by either n3-PUFA or selenium against neurotoxic contaminants.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".