Costs and benefits of joining South American sea lion breeding groups: testing the assumptions of a model of female breeding dispersion
Bibliographic record
Abstract
A recent cost-benefit model has been proposed (M.H. Cassini. 1999. Behav. Ecol. 10: 612616; M.H. Cassini. 2000. Behav. Processes, 51: 9399) to predict the dispersion of female mammals when breeding resources are distributed in fixed and predictable patches. The benefit of the model is a reduction in male harassment when females join breeding groups, and the cost is an increase in femalefemale competition for breeding resources. We tested the main assumptions of this model in a breeding colony of South American sea lions (Otaria flavescens), a sexually dimorphic, polygynous pinniped. The rate of femalefemale agonistic interactions increased with the number of females, which suggests that higher levels of femalefemale competition in denser breeding groups could reduce pup survival, owing to motherpup separation effects. The rate of malefemale interactions per female decreased with the number of females defended by a male, the trend being nonlinear, and males did not modify the frequency of interaction with females according to variations in the size of breeding groups. This evidence supports the advantage of female gregariousness in reducing the reproductive costs of interacting with males. We concluded that avoidance of male disturbance through dilution effects may have played an important role in the evolution of this species' mating system.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.003 | 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".