Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it