Intra- and intercompartmental associations between levels of organochlorines in maternal plasma, cord plasma and breast milk, and lead and cadmium in whole blood, for indigenous peoples of Chukotka, Russia
Bibliographic record
Abstract
Long-range transport of pollutants towards circumpolar regions emphasizes the need for up-to-date and reliable biological monitoring data. This paper explores the use, reliability and availability of maternal blood (MB) and plasma (MP), cord blood (CB) and plasma (CP) and mother's milk (MM) in terms of assessing exposure to persistent toxic substances (PTSs). It is concluded that MP has the best combination of availability, sensitivity in terms of number of PTSs, their detection frequency and concentrations, and physiological relevance. The study group consisted of 48 pregnant women of indigenous origin from the Chuchki district in the eastern Russian arctic. Blood, CB and MM specimens were collected from all women and MP, CP and MM were analyzed for the Arctic Monitoring and Assessment Programme (AMAP) suite of organochlorines (OCs) and metals (Pb and Cd in MB and CB). Generally speaking, the levels of PTSs coincided with those indicated in several AMAP publications from Chukotka and other areas of northern Russia. The correlations of PTS concentrations between the three body fluid compartments exceeded the minimum statistical requirements of alpha = 0.05 and beta = 0.20 for most of the compounds, with r > 0.46 except for Cd (r = 0.05); lipid adjustments for the OCs did not affect the r-values to any significant extent. The majority of the inter-OC correlations within compartments also fulfilled the indicated statistical condition. Careful consideration is given to the replacement of concentrations below the detection limit, OC detection frequency, the criteria for log-transformation of the data, analytical uncertainty, and biological variability. Practical implications of the findings are explored.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".