graynet: single-subject morphometric networks for neuroscience connectivity applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Connectivity, and network-level features in general, have proven to be valuable tools in several aspects of neuroscience research. Although network analysis is rooted in analysis of functional MRI data, it has recently gained traction in the analyses of morphometric features such as cortical thickness Such networks of anatomical covariance (derived based on distributions of features across a group of subjects) provided insight into changes caused by various brain disorders. When we individualize this approach to enable extraction of single-subject network features, they further enriched insights into abnormalities due to disease (Tijms, Seris, Willshaw, & Lawrie, 2012,Raamana et al. (2015),Palaniyappan, Park, Balain, Dangi, & Liddle (2015),Xu et al. (2017)). Moreover, these network-level features demonstrated potential for prognostic applications (Raamana et al., 2015,Raamana et al. (2014)), in addition to being robust to changes in scale and edge weight metrics (Raamana & Strother, 2017a).
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it