MétaCan
Menu
Back to cohort
Record W1516196501 · doi:10.1109/ccdc.2015.7162689

Design of a dynamic balanced spatial grasper mechanism

2015· article· en· W1516196501 on OpenAlexafffund
Dan Zhang, Bin Wei

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsOntario Tech University
FundersCanada Research Chairs
KeywordsPantographMechanism (biology)Computer scienceLinkage (software)GRASPMoment (physics)Process (computing)Mechanism designPoint (geometry)Control theory (sociology)EngineeringMathematicsPhysicsArtificial intelligenceMechanical engineeringGeometry

Abstract

fetched live from OpenAlex

In this paper, a dynamic balanced spatial grasper mechanism is proposed and designed based on the principle vector linkage. In the literature, no dynamic balanced spatial grasp mechanism can be found and here we will design a dynamic balanced spatial grasper mechanism. Through using the pantographs, the center of mass of the grasper mechanism is fixed at a still point and through symmetrical structure design of the four fingers, it is also moment balanced. The advantages of the proposed dynamic balanced grasper mechanism and the design process are discussed and the principle dimensions are derived.

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 score0.979
Threshold uncertainty score0.191

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 head0.032
GPT teacher head0.232
Teacher spread0.200 · 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

Citations1
Published2015
Admission routes2
Has abstractyes

Explore more

Same topicRobot Manipulation and LearningFrench-language works237,207