Challenging the contest vs. scramble dichotomy in social competition: mixed conditions allow disparately ranked monkeys to get equivalent food but experiencing more competition still leads to risk-averse decisions
Bibliographic record
Abstract
Introduction: Food competition is a major cost to group living. Resources vary in quality, distribution, and handling times, exerting differing competitive regimes and varied effects on individual food intake depending on dominance rank. Methods: ), a species with linear, nepotistic intragroup dominance hierarchies. We baited a multi-destination foraging array with a mixture of clumped, preferred and less clumped, less preferred rewards to observe how individuals' foraging decisions and route choices were affected by the presence and proximity of competitors. In contrast to previous experiments conducted with this group, rewards had minimal handling times and greater quantities to create a mix of scramble and contest competition. Results: We found that neither an individual's dominance rank nor the frequency with which they faced competition from a dominant competitor significantly affected their overall foraging success, suggesting that we were successful in invoking scramble competition. All individuals, regardless of rank, generally chose to prioritize the best reward at the cost of a less efficient route and increased travel time. Nonetheless, encountering dominant competitors in a higher proportion of trials made focal individuals more likely to begin trials at the nearest, less preferred reward, rather than face contest competition for the preferred, more distant platform. Discussion: Our findings suggest that though greater scramble competition minimizes differences in food intake, risk avoidance still exerts powerful effects on the foraging route choices of those experiencing competition.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".