Bibliographic record
Abstract
This repository is a hybrid C++ Python framework for flow visualization, containing: A simplified Python fluid visualization renderer and GUI based on imgui, possibly with several projects related to flow visualization. Version Notes(v0.0.1): Basic Features of PyFlowVis 2D Vector Field Visualization Vector Glyph: Visualizes the direction and magnitude of the vector field at sampled grid points using arrows or glyphs. Indicator (Seeding of Flowline): Allows interactive placement of seed points for flowline/pathline integration and visualization. Streamline/Pathline: Integrates and visualizes flowlines (streamlines at a fixed time) and pathlines (trajectories over time) from user-defined seeds. We support multiple integrators: Python based Ruler/Rk4/Rk5 integrator, with numba (njit) accerlated vector value query. CUDA based Ruler/Rk4 integrator Differentiable ODE solver(torchdiffeq) based integrator: "dopri5","dopri8","bosh3","fehlberg2","adaptive_heun". Coreline: coreline (of 2D unsteady field) extraction based on q-crterion/jacobian/velocity critical points. Scalar Field: Supports visualization of scalar fields (e.g., magnitude, vorticity) as color maps or overlays. FTLE: Computes 2D FTLE using a CUDA kernel. To use this feature, ensure you have a working PyCUDA environment. You can first run TestPyCUDA.py to verify your setup. 3D Vector Field Visualization Basic: 3 D vector glyphs, 3D pathlines,streamlines,coreline using vtkVortexCore lower Order(v||a). (wip): Other 3D features are under development. Planned features include iso-surface rendering, volume rendering of scalar field, observer-relative isosurface/pathline filtering, etc.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.233 | 0.190 |
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".