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Record W2990472151 · doi:10.1109/tmech.2019.2956148

Temperature Independent Triaxial Force and Torque Sensor for Minimally Invasive Interventions

2019· article· en· W2990472151 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2019
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsSt. Michael's HospitalToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTorqueMaterials scienceSensitivity (control systems)Measure (data warehouse)FabricationAcousticsFiber Bragg gratingWavelengthOpticsOptoelectronicsPhysicsElectronic engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

In this article, a triaxial force and torque sensor is developed and its response is evaluated. The sensor is designed for minimally invasive interventions. The fabrication method of the sensing structure is 3-D printing and the material used is a biocompatible acrylic plastic. Optical fibers are used as sensing elements in this sensor and the working principle is based on the Bragg wavelength shift of Bragg gratings. The response of the sensor is highly linear along all axes of measurement with the range of working around 0.7 N. The sensor has a high resolution of force and torque measurement which are 0.05 N and 0.1 Nmm, respectively. Experiments show that the sensor can accurately measure the applied force in a dynamic environment. Experiments are conducted to evaluate the thermal response of the sensor. The results show that the sensor can measure all components of the applied forces and torques to the surgery instruments without temperature cross-sensitivity.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.906

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 head0.237
Teacher spread0.224 · 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