MétaCan
Menu
Back to cohort
Record W2984992847 · doi:10.1145/3359207

MP Remix

2019· article· en· W2984992847 on OpenAlexaff
Hossein Salimian, Stephen Brooks, Derek Reilly

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2019
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCongruence (geometry)Computer sciencePerspective (graphical)Human–computer interactionSpace (punctuation)Virtual realityMultimediaArtificial intelligenceMathematicsGeometry

Abstract

fetched live from OpenAlex

to create an integrated space. We consider an MR configuration in which collocated collaborators work around a tabletop display, while remote collaborators wear an HMD to interact with a connected virtual environment that gives a 3D perspective, and consider the impact of varying degrees of view congruence with their collaborators. In a within-subjects study with 18 groups of 3, groups completed task scenarios involving 3D object manipulation around a physical-virtual mapped tabletop. We compare a synchronized Tabletop display baseline and two MR conditions with different levels of view congruence: Fishtank and Hover. Fishtank has a high degree of congruence as it shares a top-down perspective of the 3D objects with the tabletop collaborators. The Hover condition has less view congruence since 3D content hovers front of the remote collaborator above the table. The MR conditions yielded higher self-reported awareness and co-presence than the Tabletop condition for both collocated and remote participants. Remote collaborators significantly preferred the MR conditions for manipulating shared 3D models and communicating with their collaborators. Our findings illustrate strengths and weaknesses of both MR techniques but show that more participants preferred the less-congruent Hover condition overall. Reasons include that it facilitated interaction and viewing 3D objects.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.524

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 science0.0030.001
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 head0.047
GPT teacher head0.322
Teacher spread0.275 · 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 designBench or experimental
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

Citations8
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ACM on Human-Computer InteractionSame topicVirtual Reality Applications and ImpactsFrench-language works237,207