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Record W7116775570 · doi:10.1016/j.ifacol.2025.12.203

Swimming Dynamics of Bottlenose Dolphins: a Koopman Modeling Approach

2025· article· en· W7116775570 on OpenAlexfundno aff
Mike Reynolds, Ningshan Wang, Gabriel Antoniak, K. Alex Shorter, Kira Barton

Bibliographic record

VenueIFAC-PapersOnLine · 2025
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsnot available
FundersFisheries and Oceans CanadaDairy Farmers of OntarioNational Science Foundation
KeywordsNonlinear systemMotion (physics)PropulsionDynamic mode decompositionDynamics (music)Mode (computer interface)Control theory (sociology)Nonlinear dynamical systems

Abstract

fetched live from OpenAlex

Marine mammals rely on their flukes for propulsion. However, the forces generated by their foil-like flukes can not be measured directly due to the complexities of the marine environment. This study presents a data-driven modeling framework to investigate propulsive hydrodynamic forces during swimming. First, synthetic data was generated using a low-order simulation based on prior research to generate training data for model identification. The simulation models the two-dimensional translational motion (longitudinal and vertical) of the animal and approximates its fluking gait as a multi-linkage system. The propulsion force acting on the fluke is simulated using the principles of unsteady hydrodynamics and hydroelasticity. Subsequently, extended dynamic mode decomposition identifies a nonlinear model by lifting the original state-space into a higher-order nonlinear representation. The results demonstrate that the proposed method accurately estimates both the motion of the animal and the hydrodynamic forces exerted on it.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.619
Threshold uncertainty score0.898

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2025
Admission routes1
Has abstractyes

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