Human Exposure to Endocrine Disrupters and Semen Quality
Bibliographic record
Abstract
Reproductive pathology in the male represents about 20% of infertility cases. Male infertility may be attributed to a number of causes, including genetic and congenital abnormalities, infection, multisystemic diseases, varicocele, and others; however, a significant number of cases are idiopathic. Global declines in semen quality were suggested to be associated with enhanced exposure to environmental chemicals that act as endocrine disrupters as a result of our increased use of pesticides, plastics, and other anthropogenic materials. A significant body of toxicology data based upon laboratory and wildlife animals studies suggests that exposure to certain endocrine disrupters is associated with reproductive toxicity, including (1) abnormalities of the male reproductive tract (cryptorchidism, hypospadias), (2) reduced semen quality, and (3) impaired fertility in the adult. There is, however, a relative paucity of studies designed to measure exposure to endocrine disrupters on semen quality parameters (sperm concentration, motility, morphology). An overview of the human semen quality literature is presented that examines the role of endocrine disrupters including organochlorines (OC), dioxins, phthalates, phytoestrogens, and chemical mixtures (pesticides and tobacco smoke).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".