ViewCube
Bibliographic record
Abstract
Literally hundreds of thousands of users of 2D computer-aided design (CAD) tools are in the difficult process of transitioning to 3D CAD tools. A common problem for these users is disorientation in the abstract virtual 3D environments that occur while developing new 3D scenes. To help address this problem, we present a novel in-scene 3D widget called the ViewCube as a 3D orientation indicator and controller. The ViewCube is a cube-shaped widget placed in a corner of the window. When acting as an orientation indicator, the ViewCube turns to reflect the current view direction as the user re-orients the scene using other tools. When used as an orientation controller, the ViewCube can be dragged, or the faces, edges, or corners can be clicked on, to easily orient the scene to the corresponding view. We conducted a formal experiment to measure the performance of the ViewCube comparing: (1) ArcBall-style dragging using the ViewCube for manual view switching, (2) clicking on face/edge/corner elements of the ViewCube for automated view switching and (3) clicking on a dedicated row of buttons for automated view switching. The results indicate that users prefer and are almost twice as fast at using the ViewCube with dragging compared to clicking techniques, independent of a number of ViewCube representations that we examined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.018 |
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".