Bibliographic record
Abstract
Although mesh mapped with texture is observed by viewers, skeleton is often used as a compact and simplified form to represent a complex 3D object for backend processing, e.g. animation, similarity match from database, shape comparison and perceptual evaluation [ZY96][CGC*02][HK00], because the original object is too computationally expensive to analyze given time constraints and limited resources. Many skeletonization algorithms have been discussed in the literature including the medial axis and thinning approaches [BO04][PSB01]. However, these algorithms may not generate a stable skeleton. These techniques either focus on a volume, e.g. medical DICOM data, or mesh data. Our skeletonization method using voxels is stable and can be used for both mesh and volume data. Figure 1 shows (a) the original 3D horse model, (b) model covered with voxels on the surface after three iterations, and (c) the tightly bound voxels after running six iterations of our algorithm, giving a good approximation of the horse.
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.
How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".