ComPASS : a tool for distributed parallel finite volume discretizations on general\n unstructured polyhedral meshes
Bibliographic record
Abstract
\n The objective of the ComPASS project is to develop a parallel multiphase Darcy flow\n simulator adapted to general unstructured polyhedral meshes (in a general sense with\n possibly non planar faces) and to the parallelization of advanced finite volume\n discretizations with various choices of the degrees of freedom such as cell centres,\n vertices, or face centres. The main targeted applications are the simulation of CO2 geological storage, nuclear waste repository and\n reservoir simulations. \n The CEMRACS 2012 summer school devoted to high performance computing has been an ideal\n framework to start this collaborative project. This paper describes what has been achieved\n during the four weeks of the CEMRACS project which has been focusing on the implementation\n of basic features of the code such as the distributed unstructured polyhedral mesh, the\n synchronization of the degrees of freedom, and the connection to scientific libraries\n including the partitioner METIS, the visualization tool PARAVIEW, and the parallel linear\n solver library PETSc. The parallel efficiency of this first version of the ComPASS code\n has been validated on a toy parabolic problem using the Vertex Approximate Gradient finite\n volume spatial discretization with both cell and vertex degrees of freedom, combined with\n an Euler implicit time integration.\n
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".