Temporal Variation of Total Mercury Levels in the Hair of Pregnant Women from the MIREC (Maternal-Infant Research on Environmental Chemicals) Study
Bibliographic record
Abstract
Prenatal exposure to mercury (T-Hg) comes from both natural and anthropogenic sources. T-Hg can cross the blood-placental barrier, transfer from mother to fetus, and may be associated with future neurological (memory loss, personality changes, deafness and vision loss, attenuation of IQ) and physiological (delays in walking and talking, tremors and convulsions) dysfunctions. Hair is an optimal and non-invasive indicator of chronic T-Hg exposure. On average, scalp hair grows about 1.1 cm per month. As part of the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, hair samples of 350 women were collected within weeks after giving birth, to determine temporal variation of T-Hg from conception to delivery, and to compare these levels to levels measured in other matrices (maternal and umbilical cord blood, breast milk, and infant’s meconium). One centimeter sequential hair samples were collected, starting at the scalp and up to 12 cm. Overall, T-Hg levels decreased over the course of pregnancy. Mercury levels in hair positively correlated with levels in blood, but not with levels in meconium or breast milk. A higher mean mercury level was found in cord blood, meaning that it was able to pass the placental barrier, but did not reach the fetus, since the majority of meconium samples had mercury below the detection limit. This study generates knowledge on the monthly variation of mercury in a pan-Canadian pregnancy cohort and provides a unique opportunity to compare mercury levels from multiple matrices from pregnant women and their infants.
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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 | 0.001 |
| 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) | 0.001 | 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".