HingeSlicer: interactive exploration of volume images using extended 3D slice plane widgets
Bibliographic record
Abstract
Figure 1: Hinged slice plane widget used to examine an MR volume image of the brain. We present a 3D interaction model for exploring volume image data by extending the capabilities of 3D slice plane widgets. Our model provides the ability to navigate through a volume image in a fast, intuitive manner, using object-relative user navigation. Employ-ing a cut-fold-slide analogy, 3D slice plane widgets are rotated and translated relative to each other. The planes can be progressively cut to extend existing views and form staircase-like arrangements, minimizing occlusion and visual clutter problems that result from multiple, disconnected slice planes. Extending existing views also allows cutting actions to be easily “mended”, providing users with the ability to return to a previous “good ” view and explore again. A user makes cuts by drawing “hinge ” lines on a slice plane widget, in any orientation, dividing the slice plane into two pieces. These pieces can fold (rotate) around the hinge line or slide (translate) with respect to each other, allowing the user to retain a better con-textual understanding of the 3D spatial relationships between struc-tures and of 3D structure shape.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".