HybridAxes: An Immersive Analytics Tool With Interoperability Between 2D and Immersive Reality Modes
Bibliographic record
Abstract
Throughout the visual analytics process, users create visualizations with different dimensionalities. Research shows that in this process users benefit from being able to simultaneously see 2D and 3D modes of their data visualizations. Towards supporting this potential need, we introduce HybridAxes, an immersive visual analytics tool that allows the users to conduct their analysis at either end of the Reality-Virtuality continuum - either in 2D on desktop monitors or 3D in an immersive AR/VR environment - while enabling them to seamlessly switch between the two modes. We believe that by using our system, users will find it easier and faster to understand and analyze multi-dimensional data. An initial pilot test indicates positive trends in terms of users' performance time and usability metrics compared to the standalone desktop or AR/VR counterparts. Our preliminary results also suggest that users experience a lower cognitive load while task-switching between these virtuality modes. This reduction in mental effort causes them to perceive the system to be unobtrusive and pleasant to work with. Going forward, we plan to conduct more rigorous studies to verify our claims and to explore other research questions on this topic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".