Time Trend of Persistent Organic Pollutants and Metals in Greenlandic Inuit during 1994–2015
Bibliographic record
Abstract
Persistent organic pollutants (POPs), including polychlorinated biphenyls (PCBs), organchlorine pesticides and perfluoroalkylated substances (PFASs) and heavy metals bioaccumulate in the marine food chain in the Arctic regions, and thus, the Greenlandic population has a higher body burden due to relatively high intake of marine mammals. We assessed the temporal trend for POPs, including PCB 153; 1,1-dichloro-2,2-bis (p-chlorophenyl)-ethylene (p,p'-DDE); oxychlordane; six PFASs; mercury; lead and selenium in Inuit from Ilulissat, Nuuk, and across Greenland (including thirteen towns/districts), from 1994 to 2015. Data showed a significant annual decrease of 6.85-8.61% for PCB153, 6.67-8.61% for p,p'-DDE, 6.11-9.52% for oxychlordane, 5.92-6.76% for mercury and 6.48-9.43% for lead in Inuit women from Nuuk, Ilulissat, and across thirteen Greenlandic districts. The blood selenium level of all Greenlandic women increased 1.01% annually, while the trend direction was negative for Nuuk women. A similar pattern was seen for men across Greenland, with a yearly decrease of 11.3% for PCB 153, 8.61% for p,p'-DDE, 15.6% for oxychlordane, 13.1% for mercury and 12.2% for lead. Perfluorooctane sulfonate, perfluorohexane sulfonate and perfluorooctanoic acid significantly decreased 5.82-11.7% annually for both women and men across Greenland. For perfluorononanoic acid, perfluorodecanoic acid and perfluoroundecanoic acid, we observed an increasing trend for women across Greenland. In conclusion, there was a decreasing trend of the regulated POPs and metals but a potential increasing trend of the nonregulated PFASs in the Greenlandic population between 1994 and 2015. The continuing biomonitoring of contaminants of concern is important to protect the Arctic population heath.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".