Fight for your breeding right: hierarchy re-establishment predicts aggression in a social queue
Bibliographic record
Abstract
Social aggression is one of the most conspicuous features of animal societies, yet little is known about the causes of individual variation in aggression within social hierarchies. Recent theory suggests that when individuals form queues for breeding, variation in social aggression by non-breeding group members is related to their probability of inheriting breeding status. However, levels of aggression could also vary as a temporary response to changes in the hierarchy, with individuals becoming more aggressive as they ascend in rank, in order to re-establish dominance relationships. Using the group-living fish, Neolamprologus pulcher, we show that subordinates became more aggressive after they ascended in rank. Female ascenders exhibited more rapid increases in aggression than males, and the increased aggression was primarily directed towards group members of adjacent rather than non-adjacent rank, suggesting that social aggression was related to conflict over rank. Elevated aggression by ascenders was not sustained over time, there was no relationship between rank and aggression in stable groups, and aggression given by ascenders was not sex-biased. Together, these results suggest that the need to re-establish dominance relationships following rank ascension is an important determinant of variation in aggression in animal societies.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".