Sperm competition in a fish with external fertilization: the contribution of sperm number, speed and length
Bibliographic record
Abstract
The role of sperm number and quality in male competitiveness was investigated using in vitro fertilization experiments with bluegill (Lepomis macrochirus). Bluegill males use one of three mating tactics: 'sneakers', which streak spawn; 'satellites', which mimic females; and 'parentals', which are territorial. The in vitro experiments mimicked natural spawning by incorporating these males' mean proximity to eggs and timing of sperm release. Using a maximum-likelihood algorithm, raffle equations were fit to paternity data, which revealed a strong effect of sperm number on male competitiveness. There was no difference in sperm flagellum length, curvilinear swim speed or path linearity among the three male mating types, and these traits did not explain any additional variation in male competitiveness. It was estimated that, given closer proximity to eggs, satellites need release only 0.34 times as many sperm as parentals to obtain equal paternity. Despite being farther from the eggs and releasing sperm about half a second after parentals, sneakers need only release 0.58 times as many sperm as parentals to obtain equal paternity. Thus, the increased competitiveness of sneakers' sperm must come from a component of sperm quality other than speed or length.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".