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Record W2516303435 · doi:10.1038/srep31469

A Comparative Analysis of Sonic Defences in Bombycoidea Caterpillars

2016· article· en· W2516303435 on OpenAlexafffund
Veronica L. Bura, Akito Y. Kawahara, Jayne E. Yack

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of FloridaNational Science Foundation
KeywordsMimicryAposematismPredationSound (geography)BiologyZoologyInsectAnimal behaviorCommunicationEcologyPredatorAcousticsPsychology

Abstract

fetched live from OpenAlex

Caterpillars have long been used as models for studying animal defence. Their impressive armour, including flamboyant warning colours, poisonous spines, irritating sprays, and mimicry of plant parts, snakes and bird droppings, has been extensively documented. But research has mainly focused on visual and chemical displays. Here we show that some caterpillars also exhibit sonic displays. During simulated attacks, 45% of 38 genera and 33% of 61 species of silk and hawkmoth caterpillars (Bombycoidea) produced sounds. Sonic caterpillars are found in many distantly-related groups of Bombycoidea, and have evolved four distinct sound types- clicks, chirps, whistles and vocalizations. We propose that different sounds convey different messages, with some designed to warn of a chemical defence and others, to startle predators. This research underscores the importance of exploring acoustic communication in juvenile insects, and provides a model system to explore how different signals have evolved to frighten, warn or even trick predators.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.182

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 head0.015
GPT teacher head0.282
Teacher spread0.266 · 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 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

Citations31
Published2016
Admission routes2
Has abstractyes

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