Bibliographic record
Abstract
Freeform deformations are useful for describing a class of complex motions within an animation system. Such deformations have been described using a generalization of parametric surfaces and their application to modeling is well documented. We present a method that can be applied to implicit surfaces which are defined as an iso surface around a set of skeletal elements. The resulting surface is approximated by a polygon mesh. Shape distortions, such as "squash and stretch" are applied automatically to models in motion by warping the space in which the models exist. A model will change its shape as the function defining the warped space can change over time or the model will deform as it moves through the warped space. Our system also treats groups of skeletal implicit primitives as CSG primitives. Warping can also be applied to these primitives. Different warp functions, for example bend, taper and twist can be applied locally or globally, the contribution from each primitive is calculated using a set of warp functions associated with that primitive. The interesting shapes, and possibly nonlinear motion obtained from space warping, would be difficult to reproduce using other techniques.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".