MétaCan
Menu
Back to cohort
Record W3006657622 · doi:10.1093/beheco/araa003

Physical and social cues shape nest-site preference and prey capture behavior in social spiders

2020· article· en· W3006657622 on OpenAlexaff
Gabriella M. Najm, Angelika Pe, Jonathan N. Pruitt, Noa Pinter‐Wollman

Bibliographic record

VenueBehavioral Ecology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsMcMaster University
FundersNational Institute of General Medical SciencesNational Science Foundation of Sri LankaNational Institutes of HealthNational Science Foundation
KeywordsBiologyNest (protein structural motif)Biological dispersalAttractionPredationEcologyPreferenceHabitatSocial groupSocial animalSpiderSocial psychologyDemographyPopulationPsychology

Abstract

fetched live from OpenAlex

Abstract Animals often face conflicting demands when making movement decisions. To examine the decision process of social animals, we evaluated nest-site preferences of the social spider Stegodyphus dumicola. Colonies engage in collective web building, constructing 3D nests and 2D capture webs on trees and fences. We examined how individuals and groups decide where to construct a nest based on habitat structure and conspecific presence. Individuals had a strong preference for 3D substrates and conspecific presence. Groups were then provided with conflicting options of 3D substrates versus 2D substrates with a conspecific. Groups preferred the 3D structures without presettled conspecifics over a 2D substrate with conspecifics. When a group fragmented and individuals settled on both substrates, the minority group eventually joined the majority. Before rejoining, the collective prey capture behavior of divided groups improved with the size of the majority fragment. The costs of slow responses to prey for split groups and weak conspecific attraction may explain why dispersal is rare in these spiders.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.347

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 head0.075
GPT teacher head0.286
Teacher spread0.212 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueBehavioral EcologySame topicAnimal Behavior and ReproductionFrench-language works237,207