High fish consumption in French Polynesia and prenatal exposure to metals and nutrients.
Bibliographic record
Abstract
French Polynesians consume high quantities of fish and are therefore exposed to seafood-related contaminants such as mercury (Hg) or lead (Pb) and nutrients such as iodine, selenium and long chain polyunsaturated fatty acids (LC-PUFAs). As the developing foetus is sensitive to contaminants and nutrients, a cross-sectional study was conducted in French Polynesia in 2005-2006 to assess prenatal exposure to contaminants and nutrients through fish consumption. Two hundred and forty one (241) delivering women originating from all islands of French Polynesia were recruited and agreed to answer questions on fish consumption and gave permission to collect umbilical cord blood for metals and nutrients analyses. All parameters were found in high concentrations in cord blood samples except for lead. Mercury concentrations averaged 64.6 nmol/L (or 13 microg/L) with values ranging from 0.25 to 240 nmol/L. Of the sample, 82.5% had Hg concentrations above the US-EPA blood guide-line of 5.8 microg/L. Tuna was the fish species which contributed the most to Hg exposure. High selenium and LC-PUFAs may counterbalance the potential risk of prenatal exposure to Hg in French Polynesia. Due to the high fish consumption of mothers, Polynesian newborns are prenatally exposed to high doses of mercury. Although selenium and omega-3 fatty acids may counteract mercury toxicity, informing pregnant women on both the mercury and nutrient content of local fish species is important.
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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 | 0.000 | 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) | 0.003 | 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".