Immersive Gesture-based Interface for Effective 3D Geographic Data Manipulation
Bibliographic record
Abstract
This paper presents a framework providing a collection of techniques to enhance reliability, accuracy and overall effectiveness of gesture-based interaction applied to the Geographic Information Systems context. We propose, indeed, an interaction framework featuring a gestural interface, operated by two-hand gestures, for an enhanced manipulation of 3D geographic data immersively visualized by means of a Head Mounted Display (HMD). The interaction paradigm, exploits one-hand, two-hand and time-dependent gesture patterns to allow the user to perform inherently 3D tasks, like arbitrary object selection and manipulation, or measurements of relevant features, in a more intuitive yet accurate way. As the HMD orientation is tracked, the user is able to see a much greater amount of geographic information by simply turning its head. The preliminary experiments conducted so far shows a measurable and user-wise perceptible improvement in performing 3D interactive tasks, like the selection of particular geographic locations even on a complex 3D surface or the distance measurement between two geographic features.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.010 |
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".