Bibliographic record
Abstract
Abstract The ‘meshing engine’ of the title is a software component that generates unstructured triangular meshes of two‐dimensional triangles for a variety of contexts. The mesh generation is based on the well‐known technique of iterative Delaunay refinement, for which the Euclidean metric is intrinsic. The meshing engine is to be connected to applications specific host programs which can use a geometry that is different from the intrinsic geometry of the mesh, i.e. locally, the Euclidean plane. An application may require a surface mesh for embedding in a three‐dimensional geometry, or it might use a Riemannian metric to specify a required anisotropy in the mesh, or both. We focus on how the meshing engine can be designed to be independent of the embedding geometry of a host program but conveniently linked to it. A crucial tool for these goals is the use of an appropriate local co‐ordinate system for the triangles as seen by the meshing engine. We refer to it as the longest edge co‐ordinate system. Our reference to ‘linking’ the meshing engine and host system is both general and technical in the sense that the example meshes provided in the paper have all been generated by the same object code of a prototype of such a meshing engine linked to host programs defining different embedding geometries. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".