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Record W1189709048 · doi:10.1163/1568539x-00003306

Social status influences responses to unfamiliar conspecifics in a cooperatively breeding fish

2015· article· en· W1189709048 on OpenAlexaff
Isaac Y. Ligocki, Adam R. Reddon, Jennifer K. Hellmann, Constance M. O’Connor, Susan E. Marsh‐Rollo, Sigal Balshine, Ian M. Hamilton

Bibliographic record

VenueBehaviour · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVisitor patternCichlidFish <Actinopterygii>Social groupPsychologySocial psychologyDemographyBiologyFisherySociology

Abstract

fetched live from OpenAlex

In group living animals, individuals may visit other groups. The costs and benefits of such visits for the members of a group will depend on the attributes and intentions of the visitor, and the social status of responding group members. Using wild groups of the cooperatively breeding cichlid fish (Neolamprologus pulcher), we compared group member responses to unfamiliar ‘visiting’ conspecifics in control groups and in experimentally manipulated groups from which a subordinate the same size and sex as the visitor was removed. High-ranking fish were less aggressive towards visitors in removal groups than in control groups; low-ranking subordinates were more aggressive in the removal treatment. High-ranking females and subordinates the same size and sex as the visitor responded most aggressively toward the visitor in control groups. These results suggest that visitors are perceived as potential group joiners, and that such visits impose different costs and benefits on current group members.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.130
GPT teacher head0.321
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations15
Published2015
Admission routes1
Has abstractyes

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