Using deformations for browsing volumetric data
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
Many traditional techniques for "looking inside" volumetric data involve removing portions of the data, for example using various cutting tools, to reveal the interior. This allows the user to see hidden parts of the data, but has the disadvantage of removing potentially important surrounding contextual information. We explore an alternate strategy for browsing that uses deformations, where the user can cut into and open up, spread apart, or peel away parts of the volume in real time, making the interior visible while still retaining surrounding context. We consider various deformation strategies and present a number of interaction techniques based on different metaphors. Our designs pay special attention to the semantic layers that might compose a volume (e.g. the skin, muscle, bone in a scan of a human). Users can apply deformations to only selected layers, or apply a given deformation to a different degree to each layer, making browsing more flexible and facilitating the visualization of relationships between layers. Our interaction techniques are controlled with direct, "in place" manipulation, using pop-up menus and 3D widgets, to avoid the divided attention and awkwardness that would come with panels of traditional widgets. Initial user feedback indicates that our techniques are valuable, especially for showing portions of the data spatially situated in context with surrounding data.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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