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Record W2289799426 · doi:10.1038/srep21758

In a demanding task, three-handed manipulation is preferred to two-handed manipulation

2016· article· en· W2289799426 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

VenueScientific Reports · 2016
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersHorizon 2020 Framework Programme
KeywordsTask (project management)Computer scienceLeft handedHuman–computer interactionPhysicsEngineeringOptics

Abstract

fetched live from OpenAlex

Equipped with a third hand under their direct control, surgeons may be able to perform certain surgical interventions alone; this would reduce the need for a human assistant and related coordination difficulties. However, does human performance improve with three hands compared to two hands? To evaluate this possibility, we carried out a behavioural study on the performance of naive adults catching objects with three virtual hands controlled by their two hands and right foot. The subjects could successfully control the virtual hands in a few trials. With this control strategy, the workspace of the hands was inversely correlated with the task velocity. The comparison of performance between the three and two hands control revealed no significant difference of success in catching falling objects and in average effort during the tasks. Subjects preferred the three handed control strategy, found it easier, with less physical and mental burden. Although the coordination of the foot with the natural hands increased trial after trial, about two minutes of practice was not sufficient to develop a sense of ownership towards the third arm.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.571
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.057
GPT teacher head0.309
Teacher spread0.252 · 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