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Record W2624874882 · doi:10.1109/mra.2017.2691140

The Surgical Robot Challenge [From the Guest Editors]

2017· article· en· W2624874882 on OpenAlexaff
Robert Merrifield, Russell H. Taylor

Bibliographic record

VenueIEEE Robotics & Automation Magazine · 2017
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRoboticsSpecial sectionArtificial intelligenceMedical roboticsRobotFocus (optics)Surgical robotSoftwareComputer scienceFuture of roboticsInvasive surgeryEngineeringHuman–computer interactionMedicineSurgery

Abstract

fetched live from OpenAlex

The articles in this special section focus on new and innovative technologies in medical robotics. The opportunity for innovation in surgical robotics is growing. As robotic hardware is miniaturized and control software is made more intelligent, new ways in which surgery can be transformed by robotics are emerging. This special section highlights some exciting innovations that have either the potential to improve existing surgical procedures or introduce entirely new approaches for minimally invasive surgery.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0020.002
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0140.015

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 head0.043
GPT teacher head0.323
Teacher spread0.279 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2017
Admission routes1
Has abstractyes

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