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Record W2127967898 · doi:10.1109/vecims.2009.5068927

Authoring edutainment content through video annotations and 3D model augmentation

2009· article· en· W2127967898 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the ... IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems./Proceedings of the ... IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems · 2009
Typearticle
Languageen
FieldComputer Science
TopicVideo Analysis and Summarization
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceLeverage (statistics)VisualizationMetaphorAugmented realityAnnotationMultimediaGestureHuman–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, a real world based interaction metaphor has been adopted to facilitate learning about physical objects in an entertaining fashion. The proposed system incorporates an intuitive video annotation approach in order to catalog and author information about physical learning objects in a scene. The system uses augmented 3D visualization schemes and provides adequate visual cues in order to leverage the hand gesture and voice based interactions with the learning objects. These real world interaction techniques make the system transparent from the young learners and help them to become engaged in their learning activities.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
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 score0.726
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0000.001
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 head0.132
GPT teacher head0.289
Teacher spread0.157 · 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