A Multi-facetted Visual Analytics Tool for Exploratory Analysis of Human Brain and Function Datasets
Bibliographic record
Abstract
Brain research typically requires large amounts of data from different sources, and often of different nature. The use of different software tools adapted to the nature of each data source can make research work cumbersome and time consuming. It follows that data is not often used to its fullest potential thus limiting exploratory analysis. This paper presents an ancillary software tool called BRAVIZ that integrates interactive visualization with real-time statistical analyses, facilitating access to multi-facetted neuroscience data and automating many cumbersome and error-prone tasks required to explore such data. Rather than relying on abstract numerical indicators, BRAVIZ emphasizes brain images as the main object of the analysis process of individuals or groups. BRAVIZ facilitates exploration of trends or relationships to gain an integrated view of the phenomena studied, thus motivating discovery of new hypotheses. A case study is presented that incorporates brain structure and function outcomes together with different types of clinical data.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".