Bibliographic record
Abstract
Analyzing the brain regions for different activations corresponding to the activation input for an experimental setup of task functional MRI or a resting state functional Magnetic Resonance Imaging(fMRI) for a diagnosed or healthy control is a challenging issue as the processing data is voluminous 4D data with nearly 1,51,552 voxels for a single volume of 261 scans fMRI. The data considered for analysis consists of 10 healthy controls and 10 Attention Deficit Hyperactivity Disorder(ADHD) fMRI. The workflow starts with preprocessing the individual scan for realignment, coregistration and Normalisation to Montreal Neurological Institute (MNI) space. Single site scan visit consists of 64x64x37 voxels. Seventy independent components are obtained from processed data by data reduction, Independent Component Analysis (ICA) calculation, Back reconstruction and Component Calibration. ICA performs satisfactorily well on temporal and spatial localization. Visual medial network activation is pronounced in ADHD Controls than in healthy people. Sagittal, Axial and Coronal view of ADHD controls is obtained as component number 42.The analysis is further used for the automatic classification of healthy controls and ADHD people.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".