Raffles, roles, and the outcome of sperm competition in sockeye salmon
Bibliographic record
Abstract
In species with male alternative reproductive phenotypes, one phenotype is usually disadvantaged in mating competition. In salmonid fishes, large late-maturing males pair with nesting females and maintain close contact before and during spawning. Small early-maturing males have little contact with nesting females and, during spawning, begin to release sperm after the paired male. The effects of male phenotype and timing of ejaculation on success in sperm competition are not known. In this study, we determined paternity of offspring resulting from in vitro competitive fertilizations to examine these two aspects of sperm competition in sockeye salmon, Oncorhynchus nerka (Walbaum, 1792). When we fertilized eggs with mixtures of equal numbers of sperm from each of two male age classes, we found that success in sperm competition did not depend on male age. However, success in these competitive fertilizations did not conform to the fair raffle model of sperm competition, since paternity in most of the clutches was biased in favour of one male. When we added milt from two males sequentially to a batch of eggs, we found that sperm from the second male fertilized fewer eggs than sperm from the first male, but the difference was less than expected. In addition, a male's success when his milt was added first was not correlated with his success when his milt was added second.
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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".