Temporal variation of total mercury levels in the hair of pregnant women from the Maternal-Infant Research on Environmental Chemicals (MIREC) study
Bibliographic record
Abstract
Prenatal exposure to total mercury (T-Hg) comes from both natural and anthropogenic sources. T-Hg can cross the blood-brain and placental barriers, and may be associated with future neurological and physiological dysfunctions. Scalp hair is an optimal and non-invasive indicator of chronic T-Hg exposure. As part of the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, hair samples from 350 women were collected within weeks after giving birth, to determine temporal variations in T-Hg levels from preconception to delivery, and to compare these levels to corresponding levels measured in other matrices (maternal and umbilical cord blood, and infant’s meconium). A maximum of 12 one-cm hair segments were cut starting at the scalp; segments closer to the scalp reflected recent exposure (within the last month). For proper comparison, the hair segments were matched with the collection dates for other matrices. GM hair T-Hg levels greatly decreased during pregnancy, from 0.26 μg g−1 (preconception or full-length hair) to 0.18 μg g−1 (at delivery or segments closer to the scalp). A similar decreasing trend was found for T-Hg in maternal blood: 1st trimester (0.60 μg L−1) to 3rd trimester (0.47 μg L−1). The median hair-to-blood ratios of T-Hg levels varied from 364 (1st trimester), to 408 (3rd trimester), to 229 (cord blood). Very low T-Hg levels were detected in meconium. Mercury levels in blood and hair correlated with consumption of large predatory fish.
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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".