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Social Environment and Feeding State Influence Movement Decisions in a Web‐building Spider

2009· article· en· W1995759407 on OpenAlexaff
Maxence Salomon

Bibliographic record

VenueEthology · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSpiderResidenceMovement (music)EcologyCannibalismFeeding behaviorBiologyGeographyPredationZoologySociologyDemography

Abstract

fetched live from OpenAlex

Abstract It is well recognized that feeding rate has a major influence on the amount of movement between microhabitats for many animals. However, the role of other extrinsic and intrinsic factors, and particularly how these factors may interact, is not well understood. This three‐part study examines the movement decisions of a web‐building spider, Latrodectus hesperus , by assessing microhabitat tenacity in established spiders and by testing how the presence of conspecific neighbours and the combined influence of individual feeding state (determined by prior feeding experience) and neighbour presence influence microhabitat residence time in unestablished spiders. The results show that naturally established spiders did not leave their microhabitats readily, emphasizing the importance of choosing a profitable location. Unestablished spiders stayed longer in microhabitats occupied by conspecifics than in unoccupied ones, and there was practically no cannibalism even though neighbours shared webs. Furthermore, feeding state and neighbour presence showed an interactive effect on microhabitat residence time. When spiders were housed alone, microhabitat residence time increased with feeding state. However, in the presence of conspecifics, spiders had a low propensity to move, regardless of feeding state. Together, these results demonstrate the combined importance of grouping dynamics and feeding state in shaping movement decisions.

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 score0.963
Threshold uncertainty score0.107

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 head0.033
GPT teacher head0.290
Teacher spread0.256 · 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

Citations24
Published2009
Admission routes1
Has abstractyes

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