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Record W2738901255 · doi:10.23977/jeeem.2017.11002

Energy Efficiency of Living Creatures from the Mechanical Viewpoint

2017· article· en· W2738901255 on OpenAlexvenueno aff
Sungho Park

Bibliographic record

VenueJournal of Electrotechnology Electrical Engineering and Management · 2017
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsnot available
FundersNational Research Foundation of KoreaDongyang UniversityNational Research Foundation
KeywordsCreaturesMechanical energyTerrainVariety (cybernetics)Energy (signal processing)RobotPoint (geometry)Computer scienceSimulationEngineeringPhysicsArtificial intelligenceEcologyMathematicsBiologyGeometry

Abstract

fetched live from OpenAlex

Animals or insects can walk or run more elegant and advanced than any other legged mechanical models. They can move on a variety of terrain conditions since they have a distinct ability to adapt to varying conditions by using strictly coordinated leg mechanisms. Nature has an astonishing capability. They continuously have modified their body structures and living patterns, which makes it possible to survive and still continue their developments. Structural and locomotive characteristics of four-legged animals are copied and modelled from the mechanical view point, as body and links. The model is the robot, having 4-legs. Under the given structure and the locomotion conditions, the total energy to complete one cycle is calculated and finally suggested the optimal locomotive conditions.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.381

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 head0.003
GPT teacher head0.179
Teacher spread0.175 · 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
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

Citations0
Published2017
Admission routes1
Has abstractyes

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