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Record W2972550348 · doi:10.1111/eea.12825

Female false black widow spiders,<i>Steatoda grossa,</i>recognize webs based on physical and chemical cues

2019· article· en· W2972550348 on OpenAlexafffund
Andreas Fischer, Emmanuel Hung, Gerhard Gries

Bibliographic record

VenueEntomologia Experimentalis et Applicata · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTheridiidaeBiologySILKKairomoneSpiderEcologyZoologyPredationComputer science

Abstract

fetched live from OpenAlex

Abstract Females of the false black widow, Steatoda grossa CL Koch (Araneae: Theridiidae), invest significant energy and time weaving cobwebs. We tested the hypothesis that S. grossa females select sites for their webs based, in part, on the presence of con‐ or heterospecific webs, sensing both physical and chemical web cues. In bioassays, we offered female S. grossa a choice between an empty control frame and a frame bearing the web of a conspecific female or that of a female common house spider, Parasteatoda tepidarium CL Koch (Araneae: Theridiidae), recording (1) the time she spent, and (2) the time she spent inactive (a proxy for settling behaviour) on each frame. We also tested the effect of (1) silk micro‐ and macrostructure (wrapped‐up silk or intact web, each semiochemical‐deprived), (2) plastic webs, and (3) silk semiochemical extract on the responses of S. grossa females. Females settled on both con‐ and heterospecific webs and chose test stimuli based on their chemical and physical characteristics. Even plastic webs in cobweb‐like arrangement readily prompted settling behaviour by females. Our results suggest that web architecture, rather than web silk, mediates settling responses by female S. grossa on pre‐existing webs which may provide structural support for a new web and indicate habitat suitability.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

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 head0.013
GPT teacher head0.291
Teacher spread0.278 · 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.

Study designBench or experimental
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
Published2019
Admission routes2
Has abstractyes

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