Bibliographic record
Abstract
A variety of applications nowadays deal with complex dynamical problems where the data sets and interactions change over time. One of the ways to effectively deal with such problems is to employ Delaunay triangulation (DT). The structure however is well known to undergo significant changes when vertices are inserted or removed. A DT with dynamical updates displays visualization artifacts with non-smooth motions when viewed over time. The topic of this paper is smooth morphing of a DT under such circumstances. We address the issue by a series of simple operations carried out over time. Specifically, when a vertex is inserted at certain point, we split an existing vertex into two and slide one of them to the point. When a vertex is removed, we slide it towards one of its neighbors, and then merge the two vertices. For both cases, the DT properties are restored by collecting and then flipping illegal edges. This is performed by sliding a temporary vertex in two phases along two diagonals of the quadrilateral incident to the edge. The proposed algorithm has applications in dynamical computer graphics where temporal continuity is important. We validate the proposed algorithm by experiments.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".