A Novel Interaction Paradigm For Exploring Spatio-Temporal Data
Bibliographic record
Abstract
Complex spatio-temporal data is difficult to visualize and even further to interact with, especially by several users at the same time. However, visualization and exploration of such data are essential for experts to understand complex data environments, such as for mitigating the adverse effects of disease spread. This paper presents an alternative approach to that of current spatiotemporal data visualizations to access, interpret, and manipulate spatio-temporal datasets as a single user or as a team. Our approach uses tangible and visual tools such as mini-robots, tabletop displays and augmented reality tools, to facilitate the data exploration and interpretation. We also introduce a simple use case that illustrates one of the possible utilization of the system. While tangibles have been introduced to represent information, we are investigating manners in which we can depict even more complex datasets. Our system will provide a novel approach to manipulate 3D and 4D datasets that classic tools such as a 2D mouse or a tactile screen would not allow.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".