CIVDDD collaborative research in big data analytics and visualization
Bibliographic record
Abstract
The Centre for Innovation in Information Visualization and Data-Driven Design (CIVDDD) is a Big Data research project collaboration funded by the Ontario Research Fund — Research Excellence (ORF-RE). Research collaborators in the project include York University, OCAD University, the University of Toronto, and private sector partners (PSPs) to develop the next generation of data discovery, design, analytics, and visualization techniques for new computational tools, representational strategies, and interfaces. As the preeminent research hub for information analytics and scientific visualization in Ontario, CIVDDD has fifteen research teams in the four theme areas of Bioinformatics and Medical Applications, Interactive Visualization, Textual Visualization, and Scientific Visualization. The Workshop included a brief overview of CIVDDD research by the Principal Investigator Dr. Amir Asif, followed by three CIVDDD team presentations and demonstrations related to CASCON 2013 themes. These included: Graph Analytics and Biological Network Structures (Big Data and Cloud Computing), Social Media Data Visualization (Social Computing), and Dynamic Carbon Mapping in Urban Environments (Mobile Computing). Each Workshop presentation contained academic researchers and their private sector partner research collaborators. Each presentation was followed by a demonstration of the research application or visualization, and Q&A. An open discussion concluded the Workshop.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".