Clarifying relationships between persistent organic pollutant concentrations and age in wildlife biomonitoring: individuals, cross-sections, and the roles of lifespan and sex
Bibliographic record
Abstract
Relationships between persistent organic pollutant (POP) levels and age in wildlife biomonitoring are often interpreted as changes in contaminant burden as organisms age. However, cross-sectional body burden-age trends (CBATs) obtained from biomonitoring studies, which sample individuals of different ages at the same time, should not be confused with longitudinal body burden-age trends (LBATs) obtained by sampling the same individuals repeatedly through time. To clarify how CBATs and LBATs for wildlife species deviate from each other, and describe any impact of lifespan and sex, we used mechanistic bioaccumulation models to estimate historic longitudinal exposures of polar cod, ringed seals, beluga whales, and bowhead whales to polychlorinated biphenyl congener 153. Cross-sectional body burden-age trends were then produced by sampling resultant LBATs of successive birth cohorts at specific time points. As found previously for humans, the year of sampling relative to the year of peak environmental contamination was a critical parameter in determining male CBAT shapes. However, a similar cohort effect was not apparent for reproductive females because efficient POP loss through lactation prevented their lipids from retaining a memory of past exposure levels. Thus, lactation loss was not only responsible for the large differences between the CBATs of males and females of the same species, but also the lack of female CBAT variability through time. Cross-sectional body burden-age trend shapes varied little between species by lifespan, as long as equivalent age scales were used. However, lifespan relative to the timescale of environmental contaminant level changes did determine the extent to which CBATs resembled LBATs for males. We suggest that accounting for birth cohort and sex effects is essential when interpreting age trends in POP biomonitoring studies of long-lived species.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".