Levels and trends of persistent organic pollutants in human populations living in the Arctic
Bibliographic record
Abstract
The Arctic Monitoring Assessment Program (AMAP) is tasked with monitoring and assessing the status of environmental contaminants in the Arctic, documenting levels and trends, and producing science-based assessments. The objectives of this paper are to present the current levels of persistent organic pollutants (POPs) across the Arctic, and to identify trends and knowledge gaps as detailed in the most recent AMAP Human Health Assessment Report. Many Arctic populations continue to have elevated levels of these contaminants, and the highest levels of POPs were observed in populations from Greenland, Faroe Islands, and Nunavik (Canada), as well as populations in the coastal Chukotka district (Russia) for legacy POPs only. Concentrations of most POPs are declining in Arctic populations in regions where time trends data exist, although the declines are not consistent across all regions. The exceptions are per- and polyfluoroalkyl substances, with concentrations of some long-chain PFAS such as perfluorononanoic acid increasing in populations in Nunavik, Greenland and Sweden. This paper provides a more extensive summary of levels of contaminants in adults, pregnant women, and children across the Arctic than previous AMAP human health assessments, particularly for levels of long-chain PFAS, which are currently under consideration for inclusion in the Stockholm 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".