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Record W7015921847

Visualisation, Exploration and Characterization of Virtual Collections

2004· article· en· W7015921847 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2004
Typearticle
Languageen
FieldComputer Science
TopicUsability and User Interface Design
Canadian institutionsnot available
Fundersnot available
KeywordsRendering (computer graphics)VisualizationArchitectureVirtual machineCultural heritageVirtual realityProcess (computing)Asynchronous communication
DOInot available

Abstract

fetched live from OpenAlex

Unrestricted access to both historical and archaeological sites is highly desirable from both a research and a cultural perspective. However, due to security and preservation considerations, access is becoming more and more restricted and subject to various conditions. With the recent developments in 3D scanner technologies and photogrammetric techniques, it is now possible to acquire and create accurate models of such sites. Through the process of virtualisation, numerous virtual collections are created that need to be visualised, searched and eventually characterized. This paper presents a mobile virtual environment designed for the visualization of photorealistic high-resolution virtualised scenes and artefacts. The mobile virtual environment also includes a component for retrieving artefacts from virtual collections. This stereo virtual environment is portable and can be easily and rapidly deployed at any suitable location, for instance an archaeological site. The architecture and the implementation of the mobile virtual environment are described. This environment is characterized by a massively asynchronous architecture that optimises the rendering performances by distributing the calculations over various graphical processing units. A request broker insures the synchronization among the various components of the system. The performance of the system is illustrated through multiple examples of the visualisation of virtualized cultural heritage sites. In addition, it is shown how it is possible to describe the geometry of the artefacts by representing them with compact support feature vectors. A recurrent data mining system, based on these vectors, is presented. This system allows the characterization and exploration of the collection, through cluster analysis. The system employs the "query by example" paradigm and the knowledge of the expert in a recurrent approach, in order to identify clusters of artefacts. The virtual environment is subsequently utilised in order to perform visual data mining on the clusters, as identified during data mining, and to characterize and further explore the clusters by defining archetypes.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.031
GPT teacher head0.252
Teacher spread0.222 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2004
Admission routes1
Has abstractyes

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