Evaluation of New Visualization Approaches for Representing Uncertainty in the Recognized Maritime Picture
Bibliographic record
Abstract
This report documents the literature review and experimentation used to develop and assess visualization options to represent uncertainty in the Recognized Maritime Picture (RMP), which is the visual representation of the surface vessel picture for the Canadian maritime Area of Interest (AOI). Specifically, visualization options for the uncertainty with regards to the identity, spatial position and time lateness of surface contacts and the quality and time lateness of the sensor coverage were developed and assessed using computer-based experiments at the Humansystems (HSI ) Test Lab. Two icons (Rectangle design and Lego design) were developed to display uncertainty related to the surface contacts, in addition to background swaths with two features (fill and border) to display sensor coverage uncertainty. Search times and accuracy were explored through 6 experimentation sessions with 11 participants. The results showed a small search time advantage for the Rectangle design and small performance differences among the different designs for sensor coverage. Participants rated the workload associated with using the designs as low. All of the design options evaluated are considered to be suitable candidates for future evaluation by the operational community. This work was conducted as part of the Information Visualization and Management for Enhanced Domain Awareness in Maritime Security Applied Research Project within the Defence Research and Development Canada (DRDC) Maritime Domain Awareness (MDA) research thrust.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".