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Record W4406765041 · doi:10.1016/j.ifacol.2024.12.006

The cost of transition: modeling the swimming biomechanics of bottlenose dolphins to estimate cost of transport

2024· article· en· W4406765041 on OpenAlexfundno aff
Ningshan Wang, Gabriel Antoniak, Kira Barton, Nicole West, K. Alex Shorter

Bibliographic record

VenueIFAC-PapersOnLine · 2024
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsnot available
FundersFisheries and Oceans CanadaDairy Farmers of OntarioNational Science Foundation
KeywordsBiomechanicsBottlenose dolphinMarine engineeringAeronauticsEngineeringComputer scienceEnvironmental scienceEcologyBiologyAnatomy

Abstract

fetched live from OpenAlex

Given growing interest in emulating dolphin morphology and motion gait to design bio-inspired underwater vehicles with high performance, this article investigate bottle-nose dolphin (Tursiops truncatus) swimming behavior using tag-measured kinematics and a hydrodynamic model to estimate propulsive power, and energetic cost of transport. This article investigated the energetic costs associated with transient behavior required to reach steady state, i.e. additional costs required to reach steady state swimming speeds. To this end, movement data were collected from three animals during prescribed straight-line swimming trials to investigate swimming mechanics. The swimming speed ranges from 2m/s-5 m/s and the thrust power range from 0.1kw-1.5 kw. 239 qualified active-fluking periods were manually segmented and 277 qualified consistent-speed periods were automatically segmented from the collected dataset. A velocity-dependent hydrodynamic model to calculate propulsion efficiency from prior research is employed to calculate energetic costs of individual animals. These results provides new insights into dolphin swimming behavior in prescribed trial-swimming tasks and presents a path forward for continuous estimates of mechanical work and power from wild animals.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.668
Threshold uncertainty score0.512

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 head0.017
GPT teacher head0.267
Teacher spread0.250 · 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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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