Parental Consumption of Contaminated Sport Fish From Lake Ontario and Predicted Fecundability
Bibliographic record
Abstract
Wildlife studies suggest that consumption of contaminated fish from the Great Lakes may expose humans to polychlorinated biphenyls and persistent chlorinated pesticides. To assess whether time to pregnancy or fecundability is affected, we conducted a telephone survey in 1993 with female members of the New York State Angler Cohort Study who were considering pregnancy between 1991 and 1994 (N = 2,445). Among the 1,234 (50%) women who became pregnant, 895 (73%) had a known time to pregnancy. Upon enrollment into the cohort in 1991, both partners reported duration and frequency of Lake Ontario sport fish consumption. We estimated lifetime exposure to polychlorinated biphenyls from recent consumption and used a discrete-time analog of Cox proportional hazards analysis to estimate conditional fecundability ratios and 95% confidence intervals (CIs) for fish consumption among couples with complete exposure data who discontinued birth control to become pregnant (N = 575). Maternal consumption of fish for 3-6 years was associated with reduced fecundability (fecundability ratio = 0.75; 95% CI = 0.59-0.91), as was more than a monthly fish meal in 1991 (fecundability ratio = 0.73; 95% CI = 0.54-0.98). Our findings suggest that maternal but not paternal consumption of contaminated fish may reduce fecundability among couples attempting pregnancy.
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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 | 0.000 | 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) | 0.002 | 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".