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Record W2051169193 · doi:10.1002/cav.174

Automatic design and layout of 3D user interfaces

2007· article· en· W2051169193 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

VenueComputer Animation and Virtual Worlds · 2007
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsComputer scienceUser interfaceInterface (matter)Process (computing)Set (abstract data type)ProgrammerHuman–computer interactionSoftwareUser interface designGraphical user interfaceNatural user interfaceRange (aeronautics)Operating systemProgramming language

Abstract

fetched live from OpenAlex

Abstract The production of 3D user interfaces is complicated by the wide range of input and output devices used in 3D applications and the lack of software tools for their production. A 3D user interface that works well with one particular set of input and output devices could fail when another set of devices is used. To solve this problem the Grappl system automatically generates 3D user interfaces at run time. This paper presents the programmer interface to Grappl and some of the techniques used in its implementation. An important part of this process is placing user interface and application objects in 3D space. This is achieved by using policy techniques to automate the layout process. This paper presents some of the features and techniques that have been used in policy implementation, including relative position policy and grouping policy, with a few example applications. Copyright © 2007 John Wiley & Sons, Ltd.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.291
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