An investigation of issues and techniques in highly interactive computational visualization
Bibliographic record
Abstract
Computers have changed the nature of data visualization in two important ways. First, computers allow increasingly large data sets to be automatically processed. Thus, much research effort has focused on increasing the speed and quality of graphical rendering, to accommodate data sets having greater cardinality, resolution, number of dimensions, etc. Second, computers enable interactive exploration of different views and depictions of the same data over time. Although interactive visualization is now a norm, we argue that strategies for easing and improving this interaction are relatively underexploited, and show this in three case studies. Each study involves the design and implementation of novel techniques to aid visualization in some application domain. We assert the usefulness of a small set of guiding design goals for interactive visualization that are: to ease input, to augment output over space by increasing and improving output in any given state, and to augment output over time by using smooth transitions between states. The utility of these goals is demonstrated, firstly, through the results of each case study driven in part by the goals, and secondly, by using the experience gained from the case studies to
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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 | 0.000 |
| 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 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".