Using deformations for browsing volumetric data
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.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".