Clarifying Temporal Trend Variability in Human Biomonitoring of Polybrominated Diphenyl Ethers through Mechanistic Modeling
Bibliographic record
Abstract
Human biomonitoring data collected from individuals of the same age sampled in different years provide within-age temporal trends, which are often employed to evaluate the effectiveness of chemical regulatory policies. For polybrominated diphenyl ethers (PBDEs), this within-age temporal trend has been observed to vary between congeners and age groups. We systematically explore the mechanisms responsible for such variability through simulating human exposure via multiple exposure pathways to PBDEs released from multiple lifecycle stages. Our simulation indicates that, after new use of PBDEs is banned, emissions to the outdoor environment from use and waste disposal outlast those to the indoor environment from the indoor use phase, leading to slower decline rates in the contamination of food items sourced from the outdoor environment than that from indoors. Compared with indoor exposure pathways, the consumption of contaminated food contributes more to the exposure (i) to more hydrophobic, recalcitrant congeners, and (ii) of adults than children, which results in slower rates of decline in the within-age temporal trend of those congeners and in adults. The within-age temporal trend is influenced to a lesser extent by the elimination of PBDEs from the human body, e.g., differences in biotransformation potential of congeners, growth dilution, and pre- and postnatal exposures by children.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".