Environmental contaminants in Arctic human populations: Trends over 30 years
Bibliographic record
Abstract
INTRODUCTION: Arctic Monitoring and Assessment Programme (AMAP) monitors persistent organic pollutant (POP) levels in the Arctic populations and assesses health effects related to exposure to them. Many internationally regulated POPs persist in humans and biota, while new Emerging Contaminants of Arctic Concern (ECAC), many of which are unregulated, present additional challenges. Biomonitoring offers valuable insights into temporal trends within human matrices, revealing critical information not only about the efficacy of international regulations but also serving as an early warning system for exposure and risks for human health. METHODS: Data analyzed in this study is aggregated data presented in the AMAP Human Health in the Arctic assessments, which provide data on contaminant concentrations measured in human matrices from adults, and children across various population studies conducted in the Arctic since the 1980 s. Linear regression analyses were used to assess trends of various POPs including organochlorine (OCPs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs) and per- and polyfluoroalkyl substances (PFAS), measured over time from the Arctic populations in Finland, Norway, Sweden, Denmark, Iceland, Canada and Alaska (USA). RESULTS: Overall, decreasing trends were observed for PCBs and OCPs. Regulated PFAS showed decreasing trends, but increasing trends were observed for unregulated PFAS in certain populations. PBDEs showed decreasing or inconsistent trends in certain Arctic populations. CONCLUSIONS: Decreasing trends are observed for legacy POPs, but the trends for new emerging contaminants are inconsistent. More focus is needed on biomonitoring the new emerging contaminants of concern in the Arctic and their implications on human health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".