Bibliographic record
Abstract
3D Quad mesh plays an important role in various engineering fields. Due to improved design and model acquisition tools, as well as the need for higher accuracy, the number and complexity of these models are growing more rapidly than network bandwidth. Therefore, reducing the amount of transmission by compressing the 3D quad model is imperative. A mesh may be represented by its vertex data and its connectivity. Vertex data comprise coordinates of all the vertices and optionally the vertex colors and the associated normal vectors and textures. Connectivity captures the incidence relation between the quads of the mesh and their bounding vertices. Traditionally, the quad mesh connectivity encoding process involves triangulation and triangle mesh compression; this may introduce additional cost. Quad mesh can be compressed and decompressed linearly without triangulation. We introduce it in terms of a simple data structure, which we call the OE Table. It represents the connectivity of any manifold quad mesh as two tables, V and OE. V[i] is an integer reference to a vertex. OE[i] is an integer reference to an edge. Spirale Reversi decompression of quad mesh will be described in detail. It is possible to combine vertex data compression techniques with the connectivity compression. A lower upper bound 2.67 bits/quad for coding quad mesh connectivity is presented. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .J56. Source: Masters Abstracts International, Volume: 42-03, page: 0964. Adviser: Asish Mukopadhyay. Thesis (M.Sc.)--University of Windsor (Canada), 2003.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".