MercuNorth – monitoring mercury in pregnant women from the Arctic as a baseline to assess the effectiveness of the Minamata Convention
Bibliographic record
Abstract
Exposure to mercury (Hg) is a global concern, particularly among Arctic populations that rely on the consumption of marine mammals and fish which are the main route of Hg exposure for Arctic populations.The MercuNorth project was created to establish baseline Hg levels across several Arctic regions during the period preceding the Minamata Convention. Blood samples were collected from 669 pregnant women, aged 18-44 years, between 2010 and 2016 from sites across the circumpolar Arctic including Alaska (USA), Nunavik (Canada), Greenland, Iceland, Norway, Sweden, Northern Lapland (Finland) and Murmansk Oblast (Russia). Descriptive statistics were calculated, multiple pairwise comparisons were made between regions, and unadjusted linear trend analyses were performed.Geometric mean concentrations of total Hg were highest in Nunavik (5.20 µg/L) and Greenland (3.79 µg/L), followed by Alaska (2.13 µg/L), with much lower concentrations observed in the other regions (ranged between 0.48 and 1.29 µg/L). In Nunavik, Alaska and Greenland, blood Hg concentrations have decreased significantly since 1992, 2000 and 2010 respectively with % annual decreases of 4.7%, 7.5% and 2.7%, respectively.These circumpolar data combined with fish and marine mammal consumption data can be used for assessing long-term Hg trends and 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".