A Collaboration between Visual and Automated Analyses of Complex Flow Patterns
Bibliographic record
Abstract
A well-designed collaboration between visual and automated analyses can facilitate complex tasks performed by an analyst (l.e., user). One such task is the study of spatiotemporal (unsteady) flow fields represented by velocity vectors. Our earlier work introduced a cluster-based technique of abstracting velocity patterns for visual analysis of flows. Though these patterns convey important information, the visual analysis overloads the human cognitive abilities of identifying and tracking patterns in space and time. To address this overloading, we have injected new spatial patterns into the visual analysis and developed an automated analysis to detect the temporal changes in the velocity and spatial patterns. For aiding the user's understanding of the complex relations between the spatial and temporal characteristics of the patterns, this paper presents a collaboration between the visual and automated analyses. To assess this collaboration, we have proposed a usefulness metrics to encompass usability and utility of an analysis process. Using two complex flow datasets with multiple actuations and thousands of time instants, we have conducted a preliminary assessment of the collaboration based on this metrics. The outcomes of the assessment indicate that the collaboration aids an analyst in identifying and tracking pattern changes. Therefore, the collaboration shows potential in facilitating the study of complex flow patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".