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Record W1485470312 · doi:10.18260/edgj.v77i3.388

Visualizing Compound Rotations with Virtual Reality

2019· article· en· W1485470312 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEngineering design graphics journal · 2019
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaIrish Research Council
KeywordsVirtual realityComputer graphics (images)VisualizationComputer scienceHuman–computer interactionMathematics educationMultimediaEngineering drawingPsychologyArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Mental rotations are among the most difficult of all spatial tasks to perform, and even those with high levels of spatial ability can struggle to visualize the result of compound rotations.This pilot study investigates the use of the virtual reality-based Rotation Tool, created using the Virtual Reality Modeling Language (VRML) together with MATLAB and the Simulink 3D Animation Toolbox, to assist engineering students in the visualization of compound rotations made about both fixed and mobile reference frames.This tool allows students to verify the non-commutative nature of compound rotations, as well as the relationship between fixed and mobile frame rotations.The effectiveness of the Rotation Tool is evidenced by the improved ability of students to work through questions pertaining to compound rotations, as well as their increased confidence when doing so.

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: Empirical · Consensus signal: none
Teacher disagreement score0.805
Threshold uncertainty score0.637

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 head0.020
GPT teacher head0.232
Teacher spread0.212 · 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