Bibliographic record
Abstract
This paper presents a new front propagation technique which is extended to the applications in Ge-ometryModeling andMeshGeneration. The propagation process proposed in this technique is directly inspired from marching technology, that is all the points located on the original front are propagated along their local normal directions. The main difference between the current method and traditional marching methods lies in the way that local normal direction is computed. Traditionally, the local nor-mal directions are computed using geometric information, such as the average (or weighted) normal of neighboring points or facets surrounding the point to be propagated. In this method, the local nor-mal directions are calculated using equation ~n = ∇φ/|∇φ. φ is the solution of the minimum distance equation, ∇φ · ∇φ = 1, which is a variation of the Eikonal equation. The benefit of calculating normal directions in such a way is that self-intersections are avoided in a natural way. This proposed front propagation method is validated from two aspects: accuracy and efficiency. The proposed front propa-gation technique is successfully applied in the applications of offset surface construction and boundary layer mesh generation. Nomenclature φ The minimum Euclidean distance between any arbitrary point of computational domain to the propagated front ~n Normal vector ∇ First derivative in space Γ The front to be propagated t The sweep counter I.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".