Fertilization dynamics of sperm from different male mating tactics in bluegill (<i>Lepomis macrochirus</i>)
Bibliographic record
Abstract
Sperm competition results in the evolution of ejaculate characteristics such as high sperm density, high motility, and fast sperm swimming speed. A fundamental assumption of sperm competition theory is that ejaculates with high motility and fast-swimming sperm have an advantage with respect to fertilization success. We tested this assumption by studying the fertilization dynamics of alternative mating tactics (cuckolders and parentals) of male bluegill (Lepomis macrochirus Rafinesque, 1819). Sneakers (cuckolders) have faster swimming sperm and a higher proportion of motile sperm immediately following sperm activation than do parentals; however, these variables decline more quickly over time in sneaker sperm than in the sperm of parental males. We used a controlled fertilization experiment to test the prediction that parental males will have higher fertilization success than sneakers late in the sperm activation cycle because of the reduced rate of decline in ejaculate quality over time. We found that as the time from sperm activation increases parental sperm fertilizes more eggs than the sperm of sneakers. Our results support the idea that fertilization success is higher when ejaculates contain a higher proportion of either motile sperm or faster swimming sperm, all else being equal. In addition, after controlling for time from sperm activation, we found a significant bias in fertilization success toward parental males, suggesting that cryptic female choice might play a role in fertilization dynamics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".