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Record W2098930842 · doi:10.1109/tabletop.2007.18

Going Deeper: a Taxonomy of 3D on the Tabletop

2007· article· en· W2098930842 on OpenAlex
Tovi Grossman, Daniel Wigdor

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsTaxonomy (biology)Computer scienceRealmData scienceHuman–computer interactionDimension (graph theory)Management scienceEngineering

Abstract

fetched live from OpenAlex

Extending the tabletop to the third dimension has the potential to improve the quality of applications involving 3D data and tasks. Recognizing this, a number of researchers have proposed a myriad of display and input metaphors. However a standardized and cohesive approach has yet to evolve. Furthermore, the majority of these applications and the related research results are scattered across various research areas and communities, and lack a common framework. In this paper, we survey previous 3D tabletops systems, and classify this work within a newly defined taxonomy. We then discuss the design guidelines which should be applied to the various areas of the taxonomy. Our contribution is the synthesis of numerous research results into a cohesive framework, and the discussion of interaction issues and design guidelines which apply. Furthermore, our work provides a clear understanding of what approaches have been taken, and exposes new routes for potential research, within the realm of interactive 3D tabletops.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.224

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 communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.247
Teacher spread0.220 · 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

Quick stats

Citations54
Published2007
Admission routes2
Has abstractyes

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