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Record W2101647284 · doi:10.1109/vis.2003.10030

A Parallel Coordinates Interface for Exploratory Volume Visualization

2003· article· en· W2101647284 on OpenAlexaff
Simeon Potts, Melanie Tory, Torsten Möller

Bibliographic record

VenueIEEE Visualization · 2003
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceVolume renderingVisualizationRendering (computer graphics)Parallel coordinatesData visualizationComputer graphics (images)Volume (thermodynamics)UndoingScientific visualizationParallel renderingHuman–computer interactionComputational scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Volume data exploration and analysis are important tasks in many visual izat ion domains , includ ing medical imaging and computational fluid flow simulations. However, these tasks can be quite challenging, because effective volume rendering interfaces have not been established. With traditional volume rendering interfaces, understanding the space of available parameters, keeping track of what you have done, and undoing operations to go back to previous states are particularly difficult operations.

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)0.006
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 score0.052
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0030.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0520.013

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 head0.035
GPT teacher head0.331
Teacher spread0.296 · 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
Published2003
Admission routes1
Has abstractyes

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