A male's seminal fluid increases later competitors’ productivity
Bibliographic record
Abstract
Polyandrous females allow for sexual selection to persist after mating. In the event that females successfully mate with more than one male, sperm competition can occur. Seminal fluid proteins can indirectly affect a male's success in sperm competition through reducing the remating behaviour of females and can directly influence sperm competition through directly displacing competitor sperm or inducing females to eject it. These direct effects on competitor sperm are thought to contribute to the 'second male advantage', whereby the second male to mate sires the majority of offspring. Here, we show an additional mechanism where seminal proteins already present within a mated female appear to enhance offspring production of later competitor males, and contribute to second male advantage. Counter to expectation, increased offspring production was not due to a priming effect of greater early female productivity, nor was it through a general and consistent increase in offspring production. Instead, enhanced productivity was solely through lengthening the time that offspring are sired by the second male, indicating that seminal proteins from the first male to mate may enhance second male advantage through a presumably unintended protective effect on subsequent competitor sperm.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".