Effects of timing of nest entry and body size on the fertilization success of alternative male reproductive phenotypes of masu salmon (<i>Oncorhynchus masou</i>)
Bibliographic record
Abstract
Using a natural population of masu salmon ( Oncorhynchus masou (Brevoort, 1856)) in a stream of Shikaribetsu Lake, Hokkaido, Japan, we combined behavioural observations with genetic parentage analysis to explore the factors affecting fertilization success achieved by alternative mating tactics (fighting by large migratory males and sneaking by small mature male parr). Larger males gained priority access to females; migrant males do this by holding a guarding position near the nesting female and mature male parr do this by adopting sneaker behaviour and attending spawning groups. Status of mature male parr was related to success of nest entry but not to timing of nest entry, although the timing of nest entry influenced fertilization success of sneakers and ejaculation simultaneous with pair spawning was needed for fertilization by sneakers. The relative body size of each male who successfully spawned with a female is also likely to determine the proportion of eggs he fertilized because larger males have larger ejaculate. These results provide insight into factors relating to variation in fertilization success, how body size dimorphisms may be related to fitness, and evolution of alternative mating tactics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".