Warping an atlas derived from serial histology to 5 high-resolution MRIs
Bibliographic record
Abstract
The following collection consists of: 5 T1-weighted MR images (brain1-5), and an average of the 5 T1w images (model); 6 Surface-based representations of the left and right striatum, globus palidus and thalamus (obj); 6 atlases of the striatum, globus pallidus and thalamus for the 5 T1w and the model image; 5 full atlas label files (108 subcortical structures); 10 pseudo-MRIs for the 5 subjects (left and right hemisphere separate); 10 concatenated transforms registering the histologically-derived atlas to each T1w (left and right hemisphere separate) (xfm); 2 histologically-derived atlases with 108 subcortical structures delineated (left and right hemisphere separate); 2 histologically-derived atlases with the striatum, globus pallidus, and thalamus delineated (left and right hemisphere separate) (mask); 2 pseudo-MRIs of the histological segmentation (left and right hemisphere separate); 2 scripts for the nonlinear registation code to generate the full atlases (ANIMAL_script.sh) and final atlases of the striatum, globus pallidus, and thalamus (mask_script.sh). All MRIs, atlases and pseudo-MRIs are available in both MINC (http://www.bic.mni.mcgill.ca/ServicesSoftware/HomePage) and NIfTI (https://nifti.nimh.nih.gov/) format.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.016 |
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".