When population growth intensifies intergroup competition, female colobus monkeys free-ride less
Bibliographic record
Abstract
Abstract In many social species, intergroup aggression is a cooperative activity that produces public goods such as a safe and stable social environment and a home range containing the resources required to survive and reproduce. In this study, we investigate temporal variation in intergroup aggression in a growing population of colobus monkeys to ask a novel question: “Who stepped-up to produce these public goods when the competitive landscape changed?”. Both whole-group encounters and male incursions occurred more frequently as the population grew. Males and females were both more likely to participate in whole-group encounters when monopolizable food resources were available, indicating both sexes engaged in food defence. However, only females increasingly did so over time, suggesting that when intergroup competition intensified, it was females who increasingly invested in home range defence. Females were also more active in male incursions at high population densities, suggesting they also worked harder to maintain a safe and stable social environment over time. This is not to say that males were chronic free-riders when it came to maintaining public goods. Males consistently participated in the majority of intergroup interactions throughout the study period, indicating they may have lacked the capacity to invest more time and effort.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".