THE UNIVERSITY OF CALGARY Visualizing Uncertainty
Bibliographic record
Abstract
Uncertainty is a normal part of everyday life. It appears in the environment around us from the weather to the stock market, internally to some degree in almost every plan or decision we make, and is inherent in our daily communication, both verbal and visual. The form this uncertainty takes is often qualitative or unquantified and so fits poorly with the initial issues of representation, computability, and efficiency often the driving forces in initial visualizations of information. Understanding what may assist in visualizing uncertainty is the subject of this research. Initially I provide a literature review of existing work in uncertainty visualization. This review continues with an exploration of heuristic evaluation specifically on uncertainty visualization but then looks deeper at the process of heuristic evaluation itself. Moving toward user constraints and cognitive tasks I coalesce existing work relating to reasoning under uncertainty. From this I propose further linking and integrating the uncertainty visualizations into the process of reasoning which encompasses all visualization tasks. The second half of the dissertation turns to investigate uncertainty visualization in specific domains. In the first domain, results of research into visualizing temporal uncertainty
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.196 | 0.049 |
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".