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Record W2903617322 · doi:10.1093/beheco/ary158

Collective behavior and colony persistence of social spiders depends on their physical environment

2018· article· en· W2903617322 on OpenAlexaff
Ambika Kamath, Skylar D. Primavera, Colin M. Wright, Grant Navid Doering, Kirsten A. Sheehy, Noa Pinter‐Wollman, Jonathan N. Pruitt

Bibliographic record

VenueBehavioral Ecology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsMcMaster University
FundersNational Institute of General Medical SciencesNational Institutes of Health
KeywordsBiologyBiological dispersalNest (protein structural motif)EcologyPredationPersistence (discontinuity)HabitatPopulationDemography

Abstract

fetched live from OpenAlex

social spiders. Fences and trees represent substantially different physical environments: fences are 2-dimensional and relatively homogenous environments, whereas tree branches are 3-dimensional and relatively heterogeneous. We found that fence-dwelling colonies attack prey more quickly and with more attackers than tree-dwelling colonies in both field and controlled settings. Moreover, in the field, fence-dwelling colonies captured more prey, were more likely to persist, and had a greater number of individuals remaining at the end of the experiment than tree-dwelling colonies. Intriguingly, we also observed a greater propensity for colony fragmentation in tree-dwelling colonies than fence-dwelling colonies. Our results demonstrate that the physical environment is an important influence on the collective behavior and persistence of colonies of social spiders, and suggest multiple possible proximate and ultimate mechanisms-including variation in web complexity, dispersal behavior, and bet-hedging-by which this influence may be realized.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.447

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 studies0.0000.001
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 head0.048
GPT teacher head0.264
Teacher spread0.215 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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