Why this work is in the frame
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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.
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.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it