Hormonal and cytomorphological influences on the primary and secondary sex ratio in mammals
Bibliographic record
Abstract
The main purpose of this review article was to examine the existing and potential ways to predict and manipulate the sex of mammalian offspring using hormonal cues and treatments. We focused on the theories and research surrounding the potential endocrine and paracrine determinants of primary and secondary sex ratios in mammals; the primary sex ratio refers to sex distribution after fertilization and the secondary sex ratio refers to offspring sex. Several structural and functional differences between Y and X spermatozoa can impinge on their migration and fertilizing ability in different hormonal milieux. A variety of hormonal cues, including those acting on gamete formation, transport, and sperm-oocyte interactions, can also affect the primary sex ratio. Secondary sex ratios may be altered during the entire period leading up to birth by pre-implantation and post-implantation factors. Hormones such as estradiol, testosterone, cortisol, progesterone, and gonadotropin-releasing hormone/gonadotropins, exert an effect on offspring sex ratios, as evidenced by both in vitro and in vivo studies. The application of exogenous hormones at specific times during the female reproductive cycle/early gestation or during normal sperm production/storage in males to manipulate sex ratios would be more sustainable than currently used sex selection methods. However, hormonal interventions are still less efficacious and predictable than using sex sorted semen or prenatal diagnostics preceding embryo transfers or elective abortions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".