Bibliographic record
Abstract
Abstract : Visualizing uncertainty can be a challenging endeavour. In an attempt to minimize the challenges, this paper defines a systematic approach to designing a visual representation of uncertainty called the Uncertainty Visualization Development Strategy (UVDS). The strategy helps in the understanding of both the data and the uncertainty. The UVDS has eleven steps which include: identify the uncertainty visualization task; understanding the data that need to have their uncertainty visualized; understanding why uncertainty needs to be visualized and how the uncertainty visualization needs to help the user; deciding on the uncertainty to be visualized; deciding on a definition of uncertainty; determining the specific causes of the uncertainty; determining the causal categories of the uncertainty; determining the visualization requirements; calculating, assigning, or extracting the uncertainty; trying different uncertainty visualization techniques; and obtaining audience opinions and criticisms. The UVDS has been created specifically to help the designer produce comprehensive uncertainty visualizations, allow the designer more time to focus on the creative aspects of the work, and give those trying to understand what is behind the design a clearer understanding. As an example application of the UVDS, it is applied to current research regarding uncertainty visualization for the Canadian Recognized Maritime Picture (RMP).
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".