VectorLens: Angular Selection of Curves within 2D Dense Visualizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We investigate the selection of curves within a 2D visualization by specifying their angle or slope. Such angular selection has applications in parallel coordinates, time series visualizations, spatio-temporal movement data, etc. Our interaction technique specifies a region of interest in the visualization (with a position and diameter), a direction, and an angular tolerance, all with a single drag. We experimentally compared this angular selection technique with other techniques for selecting curves, and found that angular selection resulted in a higher number of trials that were successful on the first attempt and fewer incorrectly selected curves, and was also subjectively preferred by participants. We then present the design of a popup lens widget, called the VectorLens, that allows for easy angular selection and also allows the user to perform additional filtering operations based on type of curve. Multiple VectorLens widgets can also be instantiated to combine the results of their filtering operations with boolean operators.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it