MétaCan
Menu
Back to cohort
Record W4389328507 · doi:10.1016/j.simpa.2023.100605

MC-AR — A software suite for comparative mocap analysis in an augmented reality environment

2023· article· en· W4389328507 on OpenAlexaff
Adriaan Campo, Bavo Van Kerrebroeck, Marc Leman

Bibliographic record

VenueSoftware Impacts · 2023
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsMcGill University
FundersBijzonder Onderzoeksfonds UGentUniversiteit GentHorizon 2020 Framework ProgrammeFonds Wetenschappelijk OnderzoekInstitute of Physics and Engineering in Medicine
KeywordsKinematicsSoftwareComputer scienceSoftware packageComputer graphics (images)ViolinVirtual realitySuiteMotion captureMATLABProjection (relational algebra)Interface (matter)Human–computer interactionComputer visionMotion (physics)Acoustics

Abstract

fetched live from OpenAlex

Three different software packages are presented here: 1) A Unity package for simulating a virtual violinist in augmented reality on a HoloLens. The virtual violinist plays a pre-recorded piece, either as a 2D or a 3D projection. The piece can be started, stopped, forwarded, or rewound using a dedicated user interface. During interaction, eye movements are tracked. 2) A MATLAB motion capture package for analyzing the kinematic data of a user while interacting with the virtual violinist. 3) An R package for power analysis and Bayesian statistical analysis of the kinematic data. These software packages can be easily adapted to test the kinematic behavior of music students interacting with virtual teachers.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.159
Threshold uncertainty score0.532

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1590.020

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 head0.110
GPT teacher head0.347
Teacher spread0.238 · 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
GenreSoftware

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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueSoftware ImpactsSame topicMotor Control and AdaptationFrench-language works237,207