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Record W2914611214 · doi:10.5061/dryad.b97428m

Data from: Bark beetles use a spring-loaded mechanism to produce variable song patterns

2018· article· en· W2914611214 on OpenAlexaff
Amanda A. Lindeman, Jayne E. Yack

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsCarleton University
Fundersnot available
KeywordsSpring (device)Bark (sound)Mechanism (biology)Variable (mathematics)EcologyForestryGeographyBiologyMathematicsEngineering

Abstract

fetched live from OpenAlex

Many insects vary their song patterns to communicate different messages, but the underlying biomechanisms are often poorly understood. Here we report on the mechanics of sound production and variation in an elytro-tergal stridulator, male Dendroctonus valens bark beetles. Using ablation experiments coupled with high-speed video and audio recordings, we show that (1) chirps are produced using a stridulatory file on the left elytron (forewing) and a protrusion (plectrum) on the seventh abdominal segment, (2) chirps are produced by spring stridulation, a catch-and-release mechanism whereby the plectrum catches on a file tooth, and upon release, springs forward along the file, and (3) variability in chirp types is caused by introducing multiple catch and release events along the file to create regular interruptions. These results provide experimental evidence for the mechanics of elytrotergal stridulation, and provide insight into how an insect can incorporate variability into its acoustic repertoire using a spring-loaded mechanism.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.045
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0450.019

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 head0.030
GPT teacher head0.243
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreDataset

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

Citations1
Published2018
Admission routes1
Has abstractyes

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