An Evaluation of Two Dimensional and Three Dimensional User Interfaces for Colour Selection
Bibliographic record
Abstract
Three dimensional graphical user interfaces is a subject long studied, but due to its technical dependency on large amounts of processing power, it has not been possible to implement until recently.This is due to the recent advances of a computing technology that centers around special processors, created specifically for processing vertices and triangles, named graphical processing units or GPU's.By grouping small or large amounts of GPU's together, real time rendering with interactivity is now possible.Due to these recent advances in computing technology there has been a recent research interest in the HCI realm focused on spatial interactive devices and complementary technologies.Our own research focuses on creating and evaluating a 3D user interface system based on the fundamental principles that have made 2D user interface systems enhance user productivity and become widely adopted.3D dimensional interfaces are now becoming important because of the recent advances and changes in input devices made commercially available to the public and industry.With the increase in networking technology and the advent of low-power inexpensive integrated circuits(IC's), new types of input devices are possible that capture input in 3D space and output recorded data formatted using 3D spatial coordinates.These types of devices are perfect for manipulating objects in 3D space.This paper introduces a technology system that was created after reviewing existing research and then used to create an application on top of the system framework that provides research data on 3D user interface applications.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".