Voronoi diagram: An adaptive spatial tessellation for processes simulation
Bibliographic record
Abstract
In this chapter we discussed simulation of a dynamic process , a fluid flow in particular, that is a difficult task for the exsisting data structures which are 2D and static. A Voronoi data structure, as an alternative, can generate a tessellationthat accurately represents the geometrical, topological information of a fluid flow as well as its dynamic behavior in both static and dynamic manner. In the static or Eulerian methods, the structure assigns a volume of influence to each point and flow is assumed to be a transfer of fluid between these elements. Therefore, the change of fluid flow for each element is difference between inflow and outflow in it at a series of snapshots. In the dynamic or Lagrangian methods, data structure assigns a fixed mass of the fluid to each point. Therefore, tessellationmoves as fluid flow progress. The kinetic Voronoi diagram is also very well-adapted to freeLagrangian tessellationas it can properly update the topology, connectivity, and physical parameters of the tessellationelements when they change. This chapter is a part of an ongoing research work that proposes a kinetic data structure for the simulation of 3D dynamic contionus process in spatial context. In the research work, different issues regarding the development, implementation and application of such a data structure for the 3D simulation of fluid flow in hydrodynamics using Voronoi diagram have been studied.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".