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The Fluid Field SLIP Model: Terrestrial-Aquatic Dynamic Legged Locomotion

2021· article· en· W3205028563 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsnot available
Fundersnot available
KeywordsPaddleInverted pendulumControl theory (sociology)Slip (aerodynamics)Terrestrial locomotionMarine engineeringMechanicsComputer scienceSimulationGeologyEnvironmental scienceEngineeringPhysicsNonlinear systemAerospace engineering

Abstract

fetched live from OpenAlex

This paper describes the development of a single reduced-order dynamic model that captures running on land, running while submerged, and for the first time swimming on the surface of water. By capturing the effect of fluid forces on both the body and the leg, the Spring-Loaded Inverted Pendulum (SLIP) model is extended to operate in amphibious and aquatic regimes. Three distinct stable motion patterns, or dog-paddle type gaits are identified when swimming at the air-water interface. The model shows that, for surfaces swimming, alteration of the leg stroke frequency and length produces gaits that are either smooth and efficient or are vertically oscillatory and exhibit rapid disturbance rejection. Furthermore, when the model is examined at the physical parameter values corresponding to dogs (specifically Labrador Retrievers), the animal-based control parameters demonstrate nearly optimal performance.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.314

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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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

Quick stats

Citations9
Published2021
Admission routes1
Has abstractyes

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