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Record W2547765132 · doi:10.1139/tcsme-2009-0043

POSE SELECTION FOR THE KINEMATIC CALIBRATION OF A PROTOTYPED 4 DEGREES OF FREEDOM MANIPULATOR

2009· article· en· W2547765132 on OpenAlexaffvenue
Andrew Horne, Leila Notash

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsQueen's University
Fundersnot available
KeywordsKinematicsCalibrationParallel manipulatorDegrees of freedom (physics and chemistry)Selection (genetic algorithm)Linkage (software)Manipulator (device)Computer scienceSet (abstract data type)Artificial intelligenceControl theory (sociology)Control engineeringMathematicsEngineeringRobotic armRobotPhysicsStatisticsClassical mechanics

Abstract

fetched live from OpenAlex

This paper explores the pose selection for the kinematic calibration of the constraining linkage of a 4 degrees of freedom parallel manipulator. The aim is to select a set of poses out of a larger pool of possible poses which improve the calibration accuracy. Five different criteria have been suggested in the literature and are investigated in this article. The results show that the pose selection criteria did not significantly improve the calibration of this parallel manipulator. There was very little difference between applying the criteria and not applying it and some of the results using the criteria were worse than not using any criteria.

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 score0.815
Threshold uncertainty score0.341

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 head0.012
GPT teacher head0.201
Teacher spread0.188 · 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

Citations9
Published2009
Admission routes2
Has abstractyes

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