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Record W2545624330 · doi:10.1109/have.2004.1391883

A breeze enhances presence in a virtual environment

2005· article· en· W2545624330 on OpenAlexaff
Sylvie Noël, Sarah Dumoulin, Tara Whalen, Robin Ward, John A. Stewart, E. Lee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsSea breezeHaptic technologyComputer scienceObject (grammar)Virtual machineVirtual realitySimulationComputer visionHuman–computer interactionArtificial intelligenceMeteorologyPhysics

Abstract

fetched live from OpenAlex

Typically virtual environments are created with visual and auditory stimuli. Less often, haptic stimulation is included as well, usually in the form of force-feedback and tactile manipulators. Another possible source of haptic stimulation is moving air. In order to generate a breeze in a virtual environment, we created a breeze cannon from readily-available components. We compared four conditions: no breeze, self-generated breeze, object-generated breeze and nature-generated breeze. Participants reported feeling more immersed in the virtual environment when the breeze was caused by their own movement. Anecdotal results also suggest that moving air may help decrease simulator sickness.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score0.587

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 science0.0010.000
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 head0.016
GPT teacher head0.254
Teacher spread0.238 · 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 designOther design
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

Citations12
Published2005
Admission routes1
Has abstractyes

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