MétaCan
Menu
Back to cohort
Record W2166282476 · doi:10.1017/s0263574701003873

Design of spherical parallel mechanisms for application to laparoscopic surgery

2002· article· en· W2166282476 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

VenueRobotica · 2002
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsWorkspaceMechanism (biology)Haptic technologyComputer scienceGenetic algorithmOptimal designParallel manipulatorSimulationManipulator (device)Invasive surgeryControl theory (sociology)Artificial intelligenceRobotSurgeryControl (management)Medicine

Abstract

fetched live from OpenAlex

This paper addresses an optimal study of workspace for spherical parallel mechanism for laparoscopic surgery. The spherical parallel manipulator has been selected because of its characteristics. Two designs have been studied for maximizing their workspaces; a haptic device, as part of training system, and a laparoscope holding mechanism. The laparoscope holding mechanism has to satisfy additional constraints by minimizing the occupied space above the patient. The objective is to solve design problem to offer the maximal workspace for such mechanisms. The design of a haptic interface and the laparoscope holding mechanism based on the optimal parameters are presented. This paper presents a Genetic Algorithm (GA) approach for selecting optimal design parameters for maximizing workspace of spherical parallel mechanism.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.072
Threshold uncertainty score0.498

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 head0.028
GPT teacher head0.219
Teacher spread0.191 · 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