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Record W2889896638 · doi:10.1111/jzo.12614

Inter‐ and intraspecific differences in leaf beetle attachment on rigid and compliant substrates

2018· article· en· W2889896638 on OpenAlexaff
Dagmar Voigt, E.J. De Souza, Alexander Kovalev, Stanislav N. Gorb

Bibliographic record

VenueJournal of Zoology · 2018
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsArbutus Biopharma (Canada)
FundersBayer CropScienceBayer
KeywordsBiologyLeptinotarsaArthropod cuticlePolydimethylsiloxaneIntraspecific competitionStiffnessSubstrate (aquarium)BotanyZoologyLarvaComposite materialMaterials scienceInsectEcology

Abstract

fetched live from OpenAlex

Abstract The influence of substrate stiffness on the attachment ability of insects has been largely neglected so far. In the present study, traction experiments with adult beetles Gastrophysa viridula and Leptinotarsa decemlineata were carried out to study the influence of smooth, non‐structured surfaces, having different stiffness, on beetle attachment. Force measurements were performed with tethered walking adult insects, both males and females, intact and after removal of claws, on hydrophilic (normal) and hydrophobic (silanized) glass and four polydimethylsiloxane ( PDMS ) substrates ranging from 0.3 to 20 MP a in elastic modulus. Adult G. viridula generated higher safety factors (force/body weight) than L. decemlineata , 4.6–54.0 and 0.5–15.8 (min.–max.) respectively. The results show that the amputation of claws had no significant influence on the force generation by beetles on these smooth substrates. Males and females of both species performed best on stiffer surfaces having elastic moduli larger than 5 MP a. On the softer substrates, forces and safety factors significantly decreased. This effect was more prominent in L. decemlineata compared to the ten times lighter G. viridula . Since some natural substrates are rather soft, it is assumed that the effect of decrease in attachment ability of leaf beetles on soft substrata is of potential importance for the biology of leaf beetles.

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 score0.024
Threshold uncertainty score0.229

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 head0.024
GPT teacher head0.259
Teacher spread0.235 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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