Visualizing Uncertainty with Simulated Chromatic Aberration
Bibliographic record
Abstract
The area of uncertainty visualization attempts to determine the impact of alternative representations and evaluate their effectiveness in decision-making. Uncertainties are often an integral part of data, and model predictions often contain a significant amount of uncertain information. In this study, we explore a novel idea for a visualization to present data uncertainty using simulated chromatic aberration (CA). To produce uncertain data to visualize, we first utilized existing machine learning models to generate predictive results using public health data. We then visualize the data itself and the associated uncertainties with artificially spatially separated color channels, and the user perception of this CA representation is evaluated in a comparative user study. From quantitative analysis, it is observed that users are able to identify targets with the CA method more accurately than the comparator state-of-the-art approach. In addition, the speed of target identification was significantly faster in CA as compared to the alternative, but the subjective preferences of users do not vary significantly between the two.
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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 | 0.001 | 0.002 |
| 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".