MétaCan
Menu
Back to cohort
Record W2765717033 · doi:10.1145/3139131.3141202

In situ editing (EiS) for fulldomes

2017· article· en· W2765717033 on OpenAlexaff
François Ubald Brien, Emmanuel Durand, Jérémie Soria, Michał Seta, Nicolas Bouillot

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsSociety for Arts and Technology
Fundersnot available
KeywordsSpatializationComputer scienceInterfacingWorkflowPlug-inVisualizationRendering (computer graphics)Computer graphics (images)Human–computer interactionMultimediaComputer hardwareOperating systemArtificial intelligenceDatabase

Abstract

fetched live from OpenAlex

The creation workflow for fulldome consists today of many back and forth between the immersive space for visualization and desktop computers for editing. The EiS platform is an interactive creation tool that enables editing while being inside the immersive environment, allowing for coarse-grained 3D content editing. In this paper we present EiS, a platform geared toward editing inside fulldomes, but also possibly other immersive spaces. The core of EiS is a set of Blender (the popular 3D modeling tool) plugins adding support for VR controllers, fulldome rendering and interfacing with an external audio spatialization engine.

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: none
Teacher disagreement score0.943
Threshold uncertainty score0.165

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 head0.024
GPT teacher head0.317
Teacher spread0.293 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicInteractive and Immersive DisplaysFrench-language works237,207