Retrofitting Realities: Affordances and Limitations in Porting an Interactive Geospatial Visualization from Augmented to Virtual Reality
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.
Bibliographic record
Abstract
As Augmented Reality (AR) and Virtual Reality (VR) applications become more mainstream, developers now have a number of design decisions that must be carefully considered before choosing a device for an interactive visualization with big data. Unfortunately, understanding the true affordances and limitations of each device, and how these affect the resultant potential to support visual analytics, is still more of a black art than a science. In this paper, we highlight key design decisions and technical challenges in the context of a case study to port an interactive geospatial visualization from an AR device, the Microsoft Hololens, to a mobile VR device, the Google Daydream. Our results show that careful leveraging of backend cloud services can allow for interactive visualizations of big data to scale well across devices.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it