Bibliographic record
Abstract
The specificity of mate selection can vary from wantonly indiscriminate to extraordinarily choosy, and depends in large part on molecules expressed on the surfaces of sperm and eggs. Understanding the evolution of this specificity of gamete recognition leads to important insights into the evolution of reproductive isolation and speciation. One productive area of research has focused on genes that encode gamete recognition proteins in broadcast-spawning marine invertebrates. These gene products are relatively accessible to biochemical and cellular analyses of expression and function, and they mediate almost all of the elements of mate selection and specificity between males and females of such species. However, genetic analyses of their evolution are currently limited to a few combinations of molecules and taxa, and may miss the broader view of adaptive responses to selection on mating specificity across many genes and many types of mating systems. A transcriptomic study shows how next-generation sequencing methods and analyses could relatively easily broaden such studies to more clades, deepen those studies to include more of the interacting molecular parts that mediate gamete recognition, and eventually lead to a more complete understanding of the molecular basis for mating system variation and its evolutionary response to selection.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".