Long sperm fertilize more eggs in a bird
Bibliographic record
Abstract
Sperm competition, in which the ejaculates of multiple males compete to fertilize a female's ova, results in strong selection on sperm traits. Although sperm size and swimming velocity are known to independently affect fertilization success in certain species, exploring the relationship between sperm length, swimming velocity and fertilization success still remains a challenge. Here, we use the zebra finch (Taeniopygia guttata), where sperm size influences sperm swimming velocity, to determine the effect of sperm total length on fertilization success. Sperm competition experiments, in which pairs of males whose sperm differed only in length and swimming speed, revealed that males producing long sperm were more successful in terms of (i) the number of sperm reaching the ova and (ii) fertilizing those ova. Our results reveal that although sperm length is the main factor determining the outcome of sperm competition, complex interactions between male and female reproductive traits may also be important. The mechanisms underlying these interactions are poorly understood, but we suggest that differences in sperm storage and utilization by females may contribute to the outcome of sperm competition.
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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 | 0.000 | 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) | 0.004 | 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".