Bystanders intervene to impede grooming in Western chimpanzees and sooty mangabeys
Bibliographic record
Abstract
Grooming interactions benefit groomers, but may have negative consequences for bystanders. Grooming limits bystanders' grooming access and ensuing alliances could threaten the bystander's hierarchy rank or their previous investment in the groomers. To gain a competitive advantage, bystanders could intervene into a grooming bout to increase their own grooming access or to prevent the negative impact of others' grooming. We tested the impact of dominance rank and social relationships on grooming intervention likelihood and outcome in two sympatric primate species, Western chimpanzees (Pan troglodytes verus) and sooty mangabeys (Cercocebus atys atys). In both species, rather than increasing their own access to preferred partners, bystanders intervened mainly when an alliance between groomers could have a negative impact on them: when the lower-ranking groomer was close to the bystander in rank, when either groomer was an affiliation partner whose services they could lose, or the groomers were not yet strongly affiliated with each other. Thus, bystanders in both species appear to monitor grooming interactions and intervene based on their own dominance rank and social relationships, as well as triadic awareness of the relationship between groomers. While the motivation to intervene did not differ between species, mangabeys appeared to be more constrained by dominance rank than chimpanzees.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".