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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.881 | 0.557 |
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; both teacher heads agree on what is shown here.
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".