Environmental Contaminants and Human Infertility: Hypothesis or Cause for Concern?
Bibliographic record
Abstract
Throughout the 1980s and 1990s the crude human birth rate (live births per 1000 population) declined, indicating reduced fertility and suggesting a potential decline in fecundity (the potential to conceive). Detection of environmental contaminants in human tissues, together with reports of a global decline in semen quality, further fueled speculation that human infertility rates are increasing and environmental toxicants are potentially important causal agents associated with this change. However, there is little compelling evidence to suggest that infertility rates amongst the general population have changed over time. Moreover, recent studies suggest a rise in the fertility rates. While several studies documented increased time to pregnancy (TTP) in exposed study populations, other investigators were not able to replicate these findings. Nevertheless, studies involving occupational exposure together with results from animal experiments lend support to the conclusion that environmental contaminants potentially adversely affect fertility. Consequently, the impact of exposure to environmental contaminants on human fertility remains controversial. To test the hypothesis that environmental contaminant exposure was associated with enhanced risk of infertility, data concerning trends in fertility and infertility rates were examined to assess the impact of exposure of developing gametes to environmental contaminants. The relationship between exposure and reproductive outcomes was then examined to illustrate the range of adverse effects for reproductive toxicants with data sets of divergent depth and reliability. Data showed that only a weak association between exposure to environmental contaminants and adverse effects on human fertility exists. However, it is postulated that evidence of chemical exposure and potential health consequences of these exposures highlight the need for further research in this area.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".