Comparative Glyph-Field Trajectory Analyses with an AR+Tablet Hybrid User Interface for Geospatial Analysis Tasks
Bibliographic record
Abstract
Augmented reality (AR) supports large virtual display areas without the need for physical screens-affording more mobility to the user and displaying map-based data. Current head-worn AR devices have limited processing and rendering capabilities. Their hand-free input is imprecise. Hybrid interfaces, such as AR+tablet, can mitigate these limitations: the tablet can provide additional display fidelity in a region of interest and act as a precise input device. Used together, AR and a tablet support tasks that simultaneously require mobility and large area displays. However, more work is needed on such a system to understand the influence of glyph visualization techniques on glyph field scanning behaviours. Two glyph-based representations named Polyline and Mondrian were compared. Polyline is a shape-based technique known to be good for finding trends in desktop contexts. Mondrian is a colour-based technique. In theory, it is good for pre-attentive cursory exploration. Participants performed seminaturalistic tasks based on geospatial linear regression. Polyline induced more scrolling on the tablet because participants wanted to examine glyphs more closely. Mondrian induced more gaze movement across the AR display region, but tasks could also affect gaze. We then discuss focus+context, and colourmap design.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".