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Record W2150317538 · doi:10.5555/1161734.1162056

Two-step 3-dimensional sketching tool for new product development

2004· article· en· W2150317538 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

VenueWinter Simulation Conference · 2004
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsConcordia University
Fundersnot available
KeywordsSketchComputer sciencePoint (geometry)Engineering drawingParametric designCADProduct designDevelopment (topology)Parametric statisticsProduct (mathematics)Conceptual designVirtual realityTracingSolid modelingComputer graphics (images)Human–computer interactionArtificial intelligenceEngineeringAlgorithmMathematicsProgramming languageGeometry

Abstract

fetched live from OpenAlex

This paper discusses a two-step virtual reality based conceptual design tool that enables industrial designers to create sketches of their ideas in 3-dimensional space in real time. In the developed sketching tool, the rough shapes of products are generated by tracing the trajectory of the data-gloves worn by the designer. In the model a practical solution is provided to reduce the generation of unnecessary control points. This is achieved by representing each control point by a spherical volume. Once the rough sketching is completed, NURBS surfaces are constructed by the limited number of reference points that are selected from the initial sketch by using a virtual pen. The two-steps sketching technique enables designers to perform their artistic characteristics freely in an intuitive environment and also enables designers to generate parametric representations of the surfaces to be used in CAD/CAM systems for further analysis.

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: none
Teacher disagreement score0.844
Threshold uncertainty score0.607

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 communication0.0000.001
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 head0.038
GPT teacher head0.307
Teacher spread0.269 · 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