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Record W1719590446 · doi:10.5772/60881

Metrological Evaluation of a Novel Medical Robot and Its Kinematic Calibration

2015· article· en· W1719590446 on OpenAlexaff
Longfei Zhao, Ahmed Joubair, Pascal Bigras, Ilian A. Bonev

Bibliographic record

VenueInternational Journal of Advanced Robotic Systems · 2015
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsComputer scienceCalibrationRobotKinematicsRepeatabilityRobot calibrationComputer visionMetrologyArtificial intelligenceProcess (computing)Identification (biology)Position (finance)Robot kinematicsSimulationMobile robotMathematics

Abstract

fetched live from OpenAlex

The vessels are twisted in a longitudinal 3D space in the lower limbs of humans. Thus, it is difficult to perform an ultrasound scanning examination in this area. In this paper, a new medical parallel robot is introduced to effectively diagnose vessel disease in the lower limbs. The robot's position repeatability and accuracy are evaluated. Furthermore, the robot's accuracy is improved through a calibration process in which the kinematic parameters are identified through a simple identification approach.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.327
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2015
Admission routes1
Has abstractyes

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