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Record W2941113575 · doi:10.1145/3290605.3300243

TabletInVR

2019· article· en· W2941113575 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceHuman–computer interactionVirtual realityInterleavingAffordanceViewportVocabularySet (abstract data type)SoftwareMultimediaComputer graphics (images)Programming language

Abstract

fetched live from OpenAlex

Complex virtual reality (VR) tasks, like 3D solid modelling, are challenging with standard input controllers. We propose exploiting the affordances and input capabilities when using a 3D-tracked multi-touch tablet in an immersive VR environment. Observations gained during semi-structured interviews with general users, and those experienced with 3D software, are used to define a set of design dimensions and guidelines. These are used to develop a vocabulary of interaction techniques to demonstrate how a tablet's precise touch input capability, physical shape, metaphorical associations, and natural compatibility with barehand mid-air input can be used in VR. For example, transforming objects with touch input, "cutting" objects by using the tablet as a physical "knife", navigating in 3D by using the tablet as a viewport, and triggering commands by interleaving bare-hand input around the tablet. Key aspects of the vocabulary are evaluated with users, with results validating the approach.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.992

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)0.0010.008

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 head0.003
GPT teacher head0.200
Teacher spread0.197 · 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

Quick stats

Citations114
Published2019
Admission routes1
Has abstractyes

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