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Record W2029280694 · doi:10.1145/1328202.1328213

The virtual window simulator

2007· article· en· W2029280694 on OpenAlexaff
Eric Penner, J.R. Parker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceUSableFocus (optics)Virtual realityHead (geology)Window (computing)Orientation (vector space)Computer graphics (images)Optical head-mounted displayStereo displaySoftwareSet (abstract data type)SimulationHead-up displayComputer visionHuman–computer interactionMultimedia

Abstract

fetched live from OpenAlex

Most virtual reality systems offer the option of viewing the space using a head mounted display or a head coupled display. These can provide a comfortable way of providing a 3D display while detecting head motion and using that to change the viewing position and angle. However, head mounted displays typically have limited resolution, can create neck and eye strain, and can create user disorientation. Head coupled displays, where the display is usually projected onto a screen and the head mount is used for 3D and orientation only, are now a focus of attention in research and in production systems. They are used in, and in fact have spurred the development of, systems like the CAVE, Immersadesk, and IWall to name just three. However, their use is limited by their high cost, fixed nature, and space requirements, and a focus of research is on making head-coupled displays more easily usable and less expensive. The VirtualWindow project is a simulation of a head-coupled display that can be used to develop software for such systems without the expense of owning one, or at least without using the very expensive space. The simulator uses two webcams to perform 3D head tracking rather than instrumenting the user, and provides a set of useful operations that enhance the development and the viewing experience.

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.

How this classification was reachedexpand

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.380

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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2007
Admission routes1
Has abstractyes

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