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Record W1992941941 · doi:10.1086/678955

Hovering Flight in the Honeybee<i>Apis mellifera</i>: Kinematic Mechanisms for Varying Aerodynamic Forces

2014· article· en· W1992941941 on OpenAlex
Jason T. Vance, Douglas L. Altshuler, William Dickson, Michael H. Dickinson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysiological and Biochemical Zoology · 2014
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsUniversity of British Columbia
FundersOffice of Naval ResearchNational Institute of Neurological Disorders and StrokeNational Aeronautics and Space AdministrationNational Institutes of HealthNational Science Foundation
KeywordsWingKinematicsAerodynamic forceAngle of attackAmplitudeFlappingAerodynamicsDragWing loadingLift (data mining)Aerospace engineeringPhysicsAcousticsMechanicsEngineeringComputer scienceOpticsClassical mechanics

Abstract

fetched live from OpenAlex

During hovering flight, animals can increase the wing velocity and therefore the net aerodynamic force per stroke by increasing wingbeat frequency, wing stroke amplitude, or both. The magnitude and orientation of aerodynamic forces are also influenced by the geometric angle of attack, timing of wing rotation, wing contact, and pattern of deviation from the primary stroke plane. Most of the kinematic data available for flying animals are average values for wing stroke amplitude and wingbeat frequency because these features are relatively easy to measure, but it is frequently suggested that the more subtle and difficult-to-measure features of wing kinematics can explain variation in force production for different flight behaviors. Here, we test this hypothesis with multicamera high-speed recording and digitization of wing kinematics of honeybees (Apis mellifera) hovering and ascending in air and hovering in a hypodense gas (heliox: 21% O2, 79% He). Bees employed low stroke amplitudes (86.7° ± 7.9°) and high wingbeat frequencies (226.8 ± 12.8 Hz) when hovering in air. When ascending in air or hovering in heliox, bees increased stroke amplitude by 30%-45%, which yielded a much higher wing tip velocity relative to that during simple hovering in air. Across the three flight conditions, there were no statistical differences in the amplitude of wing stroke deviation, minimum and stroke-averaged geometric angle of attack, maximum wing rotation velocity, or even wingbeat frequency. We employed a quasi-steady aerodynamic model to estimate the effects of wing tip velocity and geometric angle of attack on lift and drag. Lift forces were sensitive to variation in wing tip velocity, whereas drag was sensitive to both variation in wing tip velocity and angle of attack. Bees utilized kinematic patterns that did not maximize lift production but rather maintained lift-to-drag ratio. Thus, our data indicate that, at least for honeybees, the overall time course of wing angles is generally preserved and modulation of wing tip velocity is sufficient to perform a diverse set of vertical flight behaviors.

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.

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 score0.459
Threshold uncertainty score0.496

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 head0.011
GPT teacher head0.204
Teacher spread0.193 · 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