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Record W2007880528 · doi:10.1163/1568539x-00003033

The role of various sensory inputs in establishing social hierarchies in crayfish

2012· article· en· W2007880528 on OpenAlexafffund
David T. Callaghan, William A. Dew, Cassidy D. Weisbord, Greg G. Pyle

Bibliographic record

VenueBehaviour · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsLakehead University
FundersCanada Research Chairs
KeywordsCrayfishAgonistic behaviourOlfactionDominance (genetics)Stimulus modalitySensory systemCommunicationPsychologyDominance hierarchyNeuroscienceEcologyBiologyAggressionSocial psychology

Abstract

fetched live from OpenAlex

Crayfish form social hierarchies through agonistic interactions. During formation of social hierarchies, individual crayfish establish dominance by signalling status through olfaction, vision and touch. Our study investigated which of these three sensory modalities played the most important role in establishing dominance in rusty crayfish (Orconectes rusticus). Olfaction, vision and touch were systematically impaired in staged triadic and dyadic agonistic interactions to determine the relative contribution of each sensory input. Our results suggest that olfaction is the most important sensory modality during the initial formation of dominance hierarchies in rusty crayfish. Using olfaction alone, crayfish were capable of communicating social status with sensory competent crayfish; without full olfactory ability crayfish were unable to effectively establish dominance. Vision and touch were also found to play practical roles in reducing unnecessary risk; with antennae for touch, functionally reducing the number of fight initiations, and vision allowing a crayfish under imminent attack to ready itself, strike first, or retreat.

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)0.000
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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

Citations23
Published2012
Admission routes2
Has abstractyes

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