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Record W2807505272 · doi:10.1088/1748-3190/aac912

At-scale lift experiments modeling dragonfly forewings

2018· article· en· W2807505272 on OpenAlexafffund
Peter Andras Kovacs Szabo, G.M.T. D’Eleuterio

Bibliographic record

VenueBioinspiration & Biomimetics · 2018
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsFlappingLift (data mining)DragonflyStructural engineeringKinematicsAcousticsMaterials scienceEngineeringAerospace engineeringWingPhysicsComputer scienceBiologyEcologyOdonataClassical mechanics

Abstract

fetched live from OpenAlex

An experimental investigation of the lift performance of an artificial platform at the scale of the dragonfly species Sympetrum sanguineum is presented. The platform, as well as the lift sensor, was custom designed and built. The flapping mechanism consisted of a piezoelectric bending-beam actuator, a transmission using carbon-fiber elements and polymide-film joints, and wings constructed of polyester film with a carbon-fiber support structure. The flapping kinematics of the Sympetrum sanguineum was replicated as closely as possible although only a pair of forewings were used in these experiments. The lift generated, when accounting for the addition of a pair of hindwings, is predicted to be sufficient to allow for the hovering of a dragonfly. The results, the first at-scale fully transient measurements of artificial dragonfly forewings, show that the lift curves quantitatively as well as qualitatively validate existing two-dimensional and three-dimensional computer simulations of dragonfly forewings.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.027
GPT teacher head0.236
Teacher spread0.209 · 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 designSimulation or modeling
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 routes2
Has abstractyes

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