Bibliographic record
Abstract
This dataset contains processed diffusion MRI data of primate subjects, DTI[1], DIAMOND[2] and fODF[3] reconstruction and metrics. Subjects included in the dataset come from 3 sites of the PRIME-DE[4] database, specifically Aix-Marseille, UC-Davis and Mount Sinai - Philips. The data was processed using mrHARDIflow[5], a pipeline tailored to process high resolution diffusion data. Please, cite the paper, as well as the PRIME-DE paper, if you use any data available here in your research. LeBihan, D., Mangin, J.-F., Poupon, C., Clark, C. A., Pappata, S., Molko, N., et al. (2001). Diffusion tensor imaging: Concepts and applications. Journal of Magnetic Resonance Imaging 13, 534–546. doi:10.1002/jmri.1076 Scherrer, B., Schwartzman, A., Taquet, M., Sahin, M., Prabhu, S. P., and Warfield, S. K. (2016). Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (diamond). Magnetic Resonance in Medicine 76, 963–977. doi:10.1002/mrm.25912 Tournier, J.-D., Calamante, F., and Connelly, A. (2007). Robust determination of the fibre orientation distribution in diffusion mri: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35, 1459–1472. doi:10.1016/j.neuroimage.2007.02.016 Milham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., et al. (2018). An open resource for non-human primate imaging. Neuron 100, 61–74. doi:10.1016/j.neuron.2018.08.039 Valcourt Caron, A., Shmuel, A., Hao, Z., and Descoteaux, M. (2021). mrHARDIflow : A pipeline tailored for the preprocessing and analysis of Multi-Resolution High Angular diffusion MRI and its application to a variability study of the PRIME-DE database. bioRxiv. doi:10.1101/2021.11.22.469616
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.054 |
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".