Investigating Intergenerational Differences in Human PCB Exposure due to Variable Emissions and Reproductive Behaviors
Bibliographic record
Abstract
BACKGROUND: Reproductive behaviors--such as age of childbearing, parity, and breast-feeding prevalence--have changed over the same historical time period as emissions of polychlorinated biphenyls (PCB) and may produce intergenerational differences in human PCB exposure. OBJECTIVES: Our goal in this study was to estimate prenatal, postnatal, and lifetime PCB exposures for women at different ages according to year of birth, and to evaluate the impact of reproductive characteristics on intergenerational differences in exposure. METHODS: We used the time-variant mechanistic model CoZMoMAN to calculate human bioaccumulation of PCBs, assuming both hypothetical constant and realistic time-variant emissions. RESULTS: Although exposure primarily depends on when an individual was born relative to the emission history of PCBs, reproductive behaviors can have a significant impact. Our model suggests that a mother's reproductive history has a greater influence on the prenatal and postnatal exposures of her children than it does on her own cumulative lifetime exposure. In particular, a child's birth order appears to have a strong influence on their prenatal exposure, whereas postnatal exposure is determined by the type of milk (formula or breast milk) fed to the infant. CONCLUSIONS: Prenatal PCB exposure appears to be delayed relative to the time of PCB emissions, particularly among those born after the PCB production phaseout. Consequently, the health repercussions of environmental PCBs can be expected to persist for several decades, despite bans on their production for > 40 years.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".