A mechanism for cryptic female choice in chinook salmon
Bibliographic record
Abstract
Female mate choice after copulation or spawning is cryptic when a female differentially influences the fertilization success of sperm from different males. We tested whether ovarian fluid could act as a potential mechanism of cryptic female choice (CFC) in chinook salmon (Oncorhynchus tshawytscha) by comparing how ovarian fluid from each of 7 females affected the sperm behavior of 11 different males. Using computer-assisted sperm analysis, we measured sperm velocity, motility, longevity, and linearity from the ejaculates of each male activated in the ovarian fluid from each female. Mean sperm swimming speed, path trajectory, and longevity differed significantly among males, and within males depended on the female's ovarian fluid in which it was activated. Most important, the pattern of within-male variation in these traits also varied significantly among males in response to different females' ovarian fluids. As sperm velocity is known to be a prime determinant of fertilization success in externally fertilizing fishes. This finding suggests that variation in the composition of female ovarian fluid may be a mechanism for CFC, whereby females differentially enhance the swimming speed of sperm from different males. Thus, female ovarian fluid can alter relative male fertilization success when there is intense sperm competition, as there is in this and other group spawning fish species. Copyright 2008, Oxford University Press.
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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 | 0.001 |
| 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".