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Record W2148594131 · doi:10.1093/beheco/arn148

Do hoverflies (Diptera: Syrphidae) sound like the Hymenoptera they morphologically resemble?

2008· article· en· W2148594131 on OpenAlexaff
Arash Rashed, Muhammad Imran Khan, Jeff W. Dawson, Jayne E. Yack, Thomas N. Sherratt

Bibliographic record

VenueBehavioral Ecology · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsBiologyMimicryHymenopteraSound (geography)Range (aeronautics)ZoologyEcologyAcoustics

Abstract

fetched live from OpenAlex

It has long been recognized that many hoverfly species (Diptera: Syrphidae) mimic the morphological appearance of defended Hymenoptera, such as wasps and bees. However, it has also been repeatedly suggested that some mimetic hoverflies respond with sounds on attack that resemble the warning or startle sounds of their hymenopteran models. In this study, we set out to quantitatively compare the spectral characteristics of the sounds produced by a range of nonmimetic flies, wasps, bumblebees, honeybees, and their hoverfly mimics when they were artificially attacked. The sounds made by wasps and honeybees after simulated attacks were statistically distinguishable from their hoverfly mimics. Bumblebee models of their hoverfly mimics share some similarities in the sound they produce on attack, but they were no closer acoustically to their model than a range of other hoverfly species that morphologically resemble other models. All the mimetic hoverflies tested in this study tended to sound similar to one another, regardless of the model they resemble morphologically. Overall, we found little evidence that mimetic hoverflies sound like their hymenopteran models on attack, and we question whether acoustic mimicry has evolved in this complex.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.101
GPT teacher head0.261
Teacher spread0.160 · 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 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

Citations30
Published2008
Admission routes1
Has abstractyes

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