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Record W2895082017 · doi:10.1109/tvcg.2018.2873724

3D Objects Clouds: Viewing Virtual Objects in Interactive Clouds

2018· article· en· W2895082017 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

VenueIEEE Transactions on Visualization and Computer Graphics · 2018
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer scienceVariety (cybernetics)AnimationAugmented realityVirtual realityGridObject (grammar)Computer graphics (images)Cloud computingHuman–computer interactionComputer animationVisualizationMultimediaArtificial intelligence

Abstract

fetched live from OpenAlex

Given the expanding use of 3D Objects in a variety of fields such as animation, gaming, virtual worlds, commerce, augmented reality and 3D printing, we present a novel system for object browsing and searching. Specifically, the system packs objects into an interactive 3D cloud for browsing and searching. It was designed with the aim of increasing search efficiency in a variety of active environments, while providing a visually engaging layout, and we evaluated this by conducting a comparative user study. We show that our system can significantly decrease search times compared to the classic grid-based layout, and it has been suggested by subjects that cloud-based searching is more interesting and visually-engaging.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.296
Teacher spread0.276 · 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