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Record W2207592786 · doi:10.1109/iros.2015.7353998

Extended bicycle model for needle steering in soft tissue

2015· article· en· W2207592786 on OpenAlexaff
Bita Fallahi, Mohsen Khadem, Carlos Rossa, Nawaid Usmani, Mahdi Tavakoli

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCurvatureConstant curvatureKinematicsImaging phantomConstant (computer programming)BevelDeformation (meteorology)PhysicsComputer scienceGeometryMathematicsEngineeringMechanical engineeringClassical mechanicsOptics

Abstract

fetched live from OpenAlex

This paper represents an extension to the kinematic bicycle model for beveled-tip needle motion in soft tissue, which accounts for non-constant curvature paths for the needle tip. For a tissue that is not stiff relative to the needle, the tissue deformation caused by needle insertion deviates the needle tip position from a constant curvature path. The proposed model is obtained by replacing the bicycle wheels with omnidirectional wheels that move in two orthogonal directions independently. Such wheels can move sideways, providing a means for modeling the deviations of the needle tip from a constant curvature path by incorporating new parameters in the model. Using an experimental setup, the needle is inserted into soft phantom tissue at different constant velocities and model parameters are fitted to experimental data. The model is verified by comparing the results from the model to empirical data.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.047
GPT teacher head0.276
Teacher spread0.229 · 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 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

Citations21
Published2015
Admission routes1
Has abstractyes

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