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Record W2170764741 · doi:10.1109/aim.2010.5695914

Experimental demonstration of a swimming robot propelled by the gradient field of a Magnetic Resonance Imaging (MRI) system

2010· article· en· W2170764741 on OpenAlexaff
Viviane Lalande, Frédérick P. Gosselin, Sylvain Martel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMagnetic fieldMagnetic resonance imagingPerpendicularRobotLift (data mining)Head (geology)PhysicsNuclear magnetic resonanceAcousticsComputer scienceArtificial intelligenceGeologyMathematicsGeometry

Abstract

fetched live from OpenAlex

Travelling inside the human body is an on-going scientific challenge. In this paper, we propose a new way of propelling robots inside the human body for gastro-intestinal applications actuated with the gradient field of an unmodified Magnetic Resonance Imaging (MRI) system. The robot is composed of a soft ferromagnetic head and a plastic tail attached together. This assembly is then placed in a bath of water inside an MRI system. The main field of the MRI is used to magnetize the head of the device while a gradient field is used to put the robot into motion. The oscillating magnetic gradient creates a force perpendicular to the direction of swimming and as the device drifts in this direction, the lift produced on its tail moves it in the forward direction. A study varying the length of the tail of the robot from 20mm to 80mm, the frequency applied and the amplitude of the gradient has been conducted and is developed in the following.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.207

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 head0.004
GPT teacher head0.216
Teacher spread0.212 · 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 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

Citations7
Published2010
Admission routes1
Has abstractyes

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