Automatic 3-D triangulations of complex geometries from a coarse hexahedral mesh
Bibliographic record
Abstract
This paper introduces a new automatic tetrahedral mesh generator on the adaptive finite \nelement ALBERTA code. The procedure can be applied to 3-D domains with boundary surfaces which \nare projectable on faces of a cube. The generalization of the mesh generator for complex domains which \ncan be split into cubes or hexahedra is straightforward. The domain surfaces must be given as analytical \nor discrete functions. Although we have worked with orthogonal and radial projections, any other oneto- \none projection may be considered. The mesh generator starts from a coarse tetrahedral mesh which is \nautomatically obtained by the subdivision of each cube into six tetrahedra. The main idea is to construct a sequence of nested meshes by refining only the tetrahedra which have a face on the cube projection faces. The virtual projection of external faces defines a triangulation on the domain boundary. The 3-D local refinement is carried out such that the approximation of domain boundary surfaces verifies a given precision: Once this objective is reached, those nodes placed on the cube faces are projected on their corresponding true boundary surfaces, and inner nodes are relocated using a linear mapping. As the mesh topology is kept during node movement, poor quality or even inverted elements could appear in the resulting mesh. For this reason, a mesh optimization procedure must be applied. The efficiency of the proposed technique is shown with several applications. On the other hand, authors are grateful for Spanish Government and FEDER support, grant contracts: CGL2004-0617l-C03-03/CLl and CGL2004-06171-C03-02/CL1.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".