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Record W2133647445 · doi:10.1098/rsbl.2010.0639

Fight for your breeding right: hierarchy re-establishment predicts aggression in a social queue

2010· article· en· W2133647445 on OpenAlexaff
Marian Y. L. Wong, Sigal Balshine

Bibliographic record

VenueBiology Letters · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAggressionDominance hierarchyDominance (genetics)BiologyRank (graph theory)HierarchyAgonistic behaviourEcologySocial groupSocial psychologyDemographyDevelopmental psychologyPsychologyGeneticsSociologyMathematicsPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.275
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations54
Published2010
Admission routes1
Has abstractyes

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