INVESTIGATING INTERGENERATIONAL DIFFERENCES IN HUMAN PCB EXPOSURE DUE TO VARIABLE EMISSIONS AND REPRODUCTIVE BEHAVIOURS
Bibliographic record
Abstract
Background and Aims: The atmospheric emissions of polychlorinated biphenyls (PCBs) have varied greatly since their introduction in the 1930s. Female reproductive characteristics have changed over the same time frame. Since both environmental chemical exposure and reproductive characteristics are expected to influence the human body burden, these concurrent trends may produce and/or enhance intergenerational differences in human PCB exposure. The objectives of this study were to determine the extent of prenatal, postnatal, and lifetime exposure of different generations and to evaluate the impact of reproductive characteristic on intergenerational differences in exposure. Methods: The non-steady state mechanistic model CoZMoMAN was used to calculate human bioaccumulation of PCBs assuming both hypothetical constant and realistic time-variant emissions. The influence of age of childbearing, parity, and breast-feeding prevalence was evaluated for three epidemiological exposure endpoints (prenatal, postnatal, and lifetime exposure). Results: The main determinant of exposure is an individual’s year of birth relative to the emission history of PCBs. Reproductive characteristics can have a significant impact but this relationship also depends on the temporal relationship with contaminant emissions. All calculations indicated that the generation born around 1980 was exposed prenatally and postnatally to more PCBs than any other generation while the generation born in 1960 experienced the greatest cumulative lifetime exposure. Conclusions: The reproductive characteristics of the mother had a greater influence on the prenatal and postnatal exposure of her infant than on her own cumulative lifetime exposure. Therefore, despite the production and use of PCBs being banned for over 40 years, the health repercussions can be expected to persist for several decades.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| 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".