Gamete compatibility genes in mammals: candidates, applications and a potential path forward
Bibliographic record
Abstract
Fertilization represents a critical stage in biology, where successful alleles of a previous generation are shuffled into new arrangements and subjected to the forces of selection in the next generation. Although much research has been conducted on how variation in morphological and behavioural traits lead to variation in fertilization patterns, surprisingly little is known about fertilization at a molecular level, and specifically about how genes expressed on the sperm and egg themselves influence fertilization patterns. In mammals, several genes have been identified whose products are expressed on either the sperm or the egg, and which influence the fertilization process, but the specific mechanisms are not yet known. Additionally, in 2014 an interacting pair of proteins was identified: 'Izumo' on the sperm, and 'Juno' on the egg. With the identification of these genes comes the first opportunity to understand the molecular aspects of fertilization in mammals, and to identify how the genetic characteristics of these genes influence fertilization patterns. Here, we review recent progress in our understanding of fertilization and gamete compatibility in mammals, which should provide a helpful guide to researchers interested in untangling the molecular mechanisms of fertilization and the resulting impacts on population biology and evolutionary processes.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".