USING CROSS-SECTIONAL BIOMONITORING STUDIES TO EXPLORE THE RELATIONSHIP BETWEEN HUMAN BODY BURDEN AND AGE
Bibliographic record
Abstract
Background and Aims: Cross-sectional data sets are a compilation of different individuals whereas the longitudinal body burden trends are for only one individual over their entire lifetime. Numerous studies have reported cross-sectional biomonitoring data of polychlorinated biphenyls (PCBs) with human body burdens increasing continuously into old age. This relationship has been previously interpreted to indicate the role of age on bioaccumulation. We propose that what has been interpreted as an increase in body burden with age is actually a result of the temporal relationship between the sampling period and the peak in emissions. The purpose of this research was to investigate which factors control the concentration versus age relationship. Methods: Population cross-sections were generated from longitudinal calculations using the mechanistic model CoZMoMAN that links emissions to the environment with human body burdens. Body burden-age trends were generated from the longitudinal body burden calculations for human exposure to hypothetical chemicals with various partitioning and degradation properties. The influence of model assumptions and emissions peakedness are also examined. Results: The temporal relationship between the emissions scenario time trend and the biomonitoring sample collection period is the most influential factor controlling the shape of concentration-age trends for population cross-sections. For chemicals with degradation half-lives of 1 year or less, the relative concentration-age trend is always the same. Published biomonitoring studies for PCBs and PBDEs are interpreted in the context of the relationship between emissions and metabolic degradation. Conclusions: Bioaccumulation does not monotonically increase with age. The main predictors of cross-sectional body burden trends with age are the amount of time elapsed after emissions peaked and the chemical degradation rate.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".