Collaborative interactive visualization: exploratory concept
Bibliographic record
Abstract
Dealing with an ever increasing amount of data is a challenge that military intelligence analysts or team of analysts face day to day. Increased individual and collective comprehension goes through collaboration between people. Better is the collaboration, better will be the comprehension. Nowadays, various technologies support and enhance collaboration by allowing people to connect and collaborate in settings as varied as across mobile devices, over networked computers, display walls, tabletop surfaces, to name just a few. A powerful collaboration system includes traditional and multimodal visualization features to achieve effective human communication. Interactive visualization strengthens collaboration because this approach is conducive to incrementally building a mental assessment of the data meaning. The purpose of this paper is to present an overview of the envisioned collaboration architecture and the interactive visualization concepts underlying the Sensemaking Support System prototype developed to support analysts in the context of the Joint Intelligence Collection and Analysis Capability project at DRDC Valcartier. It presents the current version of the architecture, discusses future capabilities to help analyst(s) in the accomplishment of their tasks and finally recommends collaboration and visualization technologies allowing to go a step further both as individual and as a team.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".