Clustering-based threshold estimation for vortex extraction and visualization
Bibliographic record
Abstract
Research efforts have been devoted to extraction and visualization of vortices in an unsteady (turbulent) flow. Characterizing the behaviors of the flow, vortices are identifiable as regions using a vortex detector known as the lambda2-criterion. Isosurface visualization renders vortex regions based on a chosen isovalue. However, it is highly challenging to choose one isovalue suitable for visualizing vortex regions of the entire flow field. A solution is the approach of maxima score that localizes vortex regions identified by the lambda2-criterion based on similarity scores relative to local extrema. The approach is however sensitive to noise or floating-point errors in the flow, leading to clutter in vortex visualization. As a feasibility study, this paper presents a threshold estimation to overcome this sensitivity. The estimation involves clustering on local minimum differences in lambda2 scalar values derived from the gradient tensor of the velocity field, and yields multiple values of the threshold without user intervention. Tested on several flows in various size and Reynolds number, the results of the threshold estimation confirmed overcoming the sensitivity of the maxima score approach. This indicates a potential of the threshold estimation to improve the robustness of the approach for vortex extraction and visualization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".