Timing Games in the Reproductive Phenology of Female Pacific Salmon (<i>Oncorhynchus</i>spp.)
Bibliographic record
Abstract
We use a game-theoretic framework to investigate the reproductive phenology of female kokanee (Oncorhynchus nerka). As in the other semelparous species of Pacific salmon, females construct nests in gravel, spawn with males, bury their fertilized eggs, and defend their nest sites until they die several days later. Later-breeding females may reuse previous nest sites, and their digging behavior is thought to subject previously buried eggs to mortality. Using game-theoretic models, we show that females can reduce this risk by allocating resources to longevity (the period between arrival and death) as opposed to eggs. Waiting before territory settlement is also expected if it allows females to conserve energy and delay senescence. The models demonstrate how these costs and benefits interact to select for a seasonal decline in longevity, a well-known phenomenon in the salmonid literature, and a seasonal decline in wait duration. Both of these predictions were supported in a field study of kokanee. Female state of reproductive maturity was the most important proximate factor causing variation in longevity and wait duration. With more than 30% of territories being reused, dig-up is likely an important selective force in this population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".