Sperm quality influences male fertilization success in walleye (<i>Sander vitreus</i>)
Bibliographic record
Abstract
We examined how variation in sperm quality influences a male's success at fertilizing ova (male fertilization success) in a wild population of walleye (Sander vitreus). To do this, we conducted controlled fertilization trials using milt and eggs (ova) from wild-spawning fish and measured male fertilization success (percentage of ova fertilized) by examining eggs after 24 h of incubation. We found that both the number of sperm and sperm swimming speed (at 10 s after activation) were significantly related to fertilization success. There was, with respect to fertilization success, a relatively large return on male investment in the number of sperm, but this return diminished as the percentage of ova fertilized increased above 50%. This is in agreement with theoretical predictions based on external fertilization dynamics. When the number of sperm used in the experimental trials was kept constant, variation in sperm swimming speed (at 10 s after activation) explained approximately 90% of the variation in a male's fertilization success. These findings demonstrate that the variation in sperm quality found in wild spawning populations has the potential to dramatically influence male reproductive success.
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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) | 0.000 | 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) | 0.001 | 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".