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Record W2388461269

Projecting beam model for robot-assisted flexible needle insertion

2011· article· en· W2388461269 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Tsinghua University(Science and Technology) · 2011
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsDeflection (physics)RobotPath (computing)Free spaceMotion planningProcess (computing)Obstacle avoidanceObstacleComputer scienceEngineeringSimulationBiomedical engineeringMaterials scienceArtificial intelligenceMobile robotPhysicsOptics
DOInot available

Abstract

fetched live from OpenAlex

Needle path steering is one of the most important techniques during robotic-assisted needle insertion.Soft tissue deformation,multi-layer tissue relative sliding and needle deflection can easily make the needle miss the target,which decreases the needle insertion precision.Based on the analysis of needle forces,a projecting beam model was proposed to predict the needle path through laws of needle deflection,with a flexible needle divided into finite segments with quasi-static process during needle insertion and each segment regarded as a projecting beam.An algorithm was then developed to predict the needle path in three-dimensional space.Simulation results show that the proposed model can effectively predict the flexible needle path,which provides a theoretical fundamental for path planning and obstacle avoidance during robot-assisted needle insertion.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.038
GPT teacher head0.216
Teacher spread0.178 · 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