TZee
Bibliographic record
Abstract
Manipulating 3D objects on a tabletop is inherently problematic. Tabletops lack a third degree of freedom and thus require novel solutions to support even the simplest 3D manipulations. Our solution is TZee - a passive tangible widget that enables natural interactions with 3D objects by exploiting the lighting properties of diffuse illumination (DI) multi-touch tabletops. TZee is assembled from stacked layers of acrylic glass to extend the tabletop's infrared light slightly above the surface without supplemental power. With TZee, users can intuitively scale, translate and rotate objects in all three dimensions, and also perform more sophisticated gestures, like "slicing" a volumetric object, that have not been possible with existing tabletop interaction schemes. TZee is built with affordable and accessible materials, and one tabletop surface can easily support multiple TZees. Moreover, since TZee is transparent, there are numerous possibilities to augment interactions with feedback, helpful hints, or other visual enhancements. We discuss several important design considerations and demonstrate the value of TZee with several applications.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.347 | 0.156 |
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".