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Record W1985042097 · doi:10.4018/jmdem.2011040101

A Real-Time 3D Visualization Framework for Multimedia Data Management, Simulation, and Prediction

2011· article· en· W1985042097 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

VenueInternational Journal of Multimedia Data Engineering and Management · 2011
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsGovernment of AlbertaUniversity of Alberta
Fundersnot available
KeywordsGeospatial analysisComputer scienceVisualizationGeographic information systemData scienceData visualizationGeovisualizationEvent (particle physics)Data miningInformation visualizationCartographyGeography

Abstract

fetched live from OpenAlex

Geographic Information Systems (GISs), which map spatiotemporal event data on geographical maps, have proven to be useful in many applications. Time-based Geographic Information Systems (GISs) allow practitioners to visualize collected data in an intuitive way. However, while current GIS systems have proven to be useful in post hoc analysis and provide simple two-dimensional geographic visualizations, their design typically lacks the features necessary for highly targeted real-time surveillance with the goal of spread prevention. This paper outlines the design, implementation, and usage of a 3D framework for real-time geospatial temporal visualization. In this case study, using livestock movements, the authors show that the framework is capable of tracking and simulating the spread of epidemic diseases. Although the application discussed in this paper relates to livestock disease, the proposed framework can be used to manage and visualize other types of high-dimensional multimedia data as well.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.915
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
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 head0.046
GPT teacher head0.333
Teacher spread0.287 · 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