Bibliographic record
Abstract
On traditional tables, people frequently use the third dimension to pile, sort and store objects. However, while effective and informative for organization, this use of the third dimension does not usually extend far above the table. To enrich interaction with digital tables, we present the concept of shallow-depth 3D -- 3D interaction with limited depth. Within this shallow-depth 3D environment several common interaction methods need to be reconsidered. Starting from any of one, two and three touch points, we present interaction techniques that provide control of all types of 3D rotation coupled with translation (6DOF) on a direct-touch tabletop display. The different techniques exemplify a wide range of interaction possibilities: from the one-touch technique, which is designed to be simple and natural, but inherits a degree of imprecision from its simplicity; through to three-touch interaction, which allows precise bimanual simultaneous control of multiple degrees of freedom, but at the cost of simplicity. To understand how these techniques support interaction in shallow-depth 3D, we present a user study that examines the efficiency of, and preferences for, the techniques developed. Results show that users are fastest and most accurate when using the three-touch technique and that their preferences were also strongly in favour of the expressive power available from three-touch.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".