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Record W2937926691 · doi:10.1242/jeb.204214

Muscles brake and bend joints to shape wings

2019· article· en· W2937926691 on OpenAlexaboutno aff
Kathryn Knight

Bibliographic record

VenueJournal of Experimental Biology · 2019
Typearticle
Languageen
FieldEngineering
TopicMechanics and Biomechanics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBird flightAeronauticsFlappingWingBrakeAnatomyEngineeringMechanical engineeringMedicineAerospace engineering

Abstract

fetched live from OpenAlex

The courage of the earliest human flight pioneers is genuinely inspiring. Fortunately, many of these innovators emerged relatively unscathed from the wreckage of their unsuccessful attempts, with the least successful models – usually based on flapping – never leaving the ground. Yet birds rarely endure the undignified collisions experienced by the first human aviators. ‘Birds are capable of diverse flight behaviours and manoeuvres’, says Jolan Thériault from the University of British Columbia (UBC), Canada, explaining that most of our current understanding of the mechanisms that allow birds to remain aloft is based on studies of the pectoral muscles, which power flight. However, much of bird's agility depends on the subtle ways in which they adjust the shape of their wings as they weave and dart through the air. ‘The contribution of the wing muscles has received relatively little attention’, says Thériault, who decided, with colleagues Joseph Bahlman (California State University, Sacramento) and Doug Altshuler and Bob Shadwick, also from UBC, to investigate how the humerotriceps muscle, which sits behind the humerus and extends the wing elbow joint, functions when flapping pigeons fly.Muscles can absorb energy to function as brakes, in addition to consuming energy when contracting to bend limbs, so the team decided to measure the amount of energy generated or absorbed by the humerotriceps muscle during different muscle activation cycles that occur at different stages of wing beats. As nerve signals trigger muscles to either consume energy and contract, or absorb energy when lengthening and behaving like brakes, Thériault was able to take advantage of measurements of muscle length changes in response to nerve signals in flying pigeons, which had been previously recorded by Angie Berg Robertson and Andy Biewener. She used these values to simulate how the muscle performs during flight activation cycles in the lab, measuring the forces produced as the muscle contracted and as they absorbed energy. Next, Thériault plotted the force and muscle length values for each activation cycle on graphs to calculate the power generated, or absorbed, to find out how the humerotriceps muscle was contributing to shaping the wing during each wing beat.Comparing the muscle's performances, it was clear that it contributed to extending the wing, generating force to spread the wing wide. However, the team could see the muscle absorbing energy, like a brake, during other activation cycles, which they suggest could hold the joint steady as the pigeon folds the wing during the upstroke of the wingbeat. And when they analysed the shape of some of the graphs where they had plotted force against muscle length, it looked as if the muscle could also store energy while stabilising the joint, ready for later use, much like a spring. Thériault also realised that, more impressively, the muscle could switch between exerting force to extend the joint and functioning as a brake within a single activation cycle and she suggests, ‘birds could adjust wing shape by changing activation and/or length-change patterns, effectively helping them produce different flight behaviours’.So pigeons are capable of fine-tuning how they use the muscles that control wing shape during flight and the team is eager to find out how these animals put their muscle versatility into practice to control their legendary manoeuvrability.

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 score0.012
Threshold uncertainty score0.294

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 head0.014
GPT teacher head0.247
Teacher spread0.233 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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