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
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it