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Record W25749164 · doi:10.1134/s0006297914130070

Proceedings of the workshop on Virtual environments 2002

2002· article· en· W25749164 on OpenAlexafffund
Wolfgang Stuerzlinger, Stefan Müller

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsYork University
FundersCanadian Institutes of Health Research
KeywordsComputer scienceUsabilityFocus (optics)Event (particle physics)Field (mathematics)Virtual realityUser interfaceInterface (matter)Human–computer interactionVirtual machineMultimediaWorld Wide Web

Abstract

fetched live from OpenAlex

This book contains the proceedings of the Eighth EUROGRAPHICS Workshop on Virtual Environments. The event was held in Barcelona from May 30 to May 31, 2002. The workshop brought together scientists, developers, and users from all over the world to present and discuss the latest scientific advances in the field of Virtual Environments.60 papers were submitted for reviewing and 22 were selected to be presented at the workshop. Most of the top research institutions working in the area submitted papers and presented their latest results. The presentations were complemented by two keynote talks from Marc Mine (Walt Disney Imagineering - VR Studio) and Eric Badique (European Commission - IST program).The research presented at this workshop can be classified into the following aspects of Virtual Environments (VEs): Input and Output Devices, Interaction and Navigation, Evaluation, Collaboration, Systems, and Applications. Devices focus mainly on hardware issues to interface with a simulation, whereas Interaction and Navigation techniques investigate how the raw input is best interpreted so that the user can easily achieve his/her goals. Evaluations provide data about the usability of Virtual Environments, and the importance of objective and reproducible studies can only be stressed. Virtual Environments are often used to communicate and to co-operate and this focus is evident in the research on Collaboration. The area of Systems discusses the various tradeoffs in building complete solutions and last, but not least, applications report on the transfer of research results into the real world. However, the reader should note that many contributions cross these boundaries, which reflects the multidisciplinary nature of Virtual Environments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.101
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1010.044

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 head0.030
GPT teacher head0.232
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations41
Published2002
Admission routes2
Has abstractyes

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