Intelligent systems for interactive design and visualization
Bibliographic record
Abstract
Intelligent systems for interactive design and visualization require technologies that reliably generate surface and solid models from acquired spatial data, user hand gestures and verbal instructions; and seamlessly integrate this information into the overall product design process. The deformable spherical self-organizing feature map (SOFM) is a versatile modeling tool that is able to create 3D shapes from numerous arbitrarily ordered N-dimensional data vectors. The data may be surface points on existing objects or multi-dimensional feature vectors obtained through experimental observation. The SOFM develops a topologically ordered lattice that provides information about magnitude and connectivity between neighboring vectors in the original data space. The shapes generated by the deformable SOFM can be displayed, reoriented, analyzed, and modified in an immersive virtual reality environment (IVR). This paper describes how the spherical SOFM can be used to reconstruct the shape of an existing object from measured coordinate points and be modified using shape transformation techniques for virtual 3D free-form design.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.126 | 0.051 |
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".