Health Risk Modifiers of Exposure to Persistent Pollutants among Indigenous Peoples of Chukotka
Bibliographic record
Abstract
The aim of the study was to assess temporal trends in health risks related to most common persistent contaminants, including polychlorinated biphenyls (PCBs), dichloro-diphenyl-trichloroethanes (DDTs), lead (Pb), as well as mercury (Hg) among indigenous peoples living in coastal areas of Chukotka in Arctic Russia. This is examined in relation to exposure pathways and a range of social and behavioral factors capable of modifying the exposure to these contaminants, including place of residence, income, traditional subsistence, alcohol consumption, and awareness of risk prevention. The primary exposure pathway for PCBs is shown to be the intake of traditional foods, which explained as much as 90% of the total health risk calculated employing established risk guidelines. Nearly 50% of past DDT-related health risks also appear to have been contributed by contaminated indoor surfaces involving commonly used DDT-containing insecticides. Individuals who practiced traditional activities are shown to have experienced a 4.4-fold higher risk of exposure to PCBs and a 1.3-fold higher risk for DDTs, Pb, and Hg. Low income, high consumption of marine mammal fat, alcohol consumption, and lack of awareness of health risk prevention are attributed to a further 2- to 6-fold increase in the risk of PCBs exposure. Low socioeconomic status enhances the health risks associated with exposure to the persistent contaminants examined.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".