HeuDiConv — flexible DICOM conversion into structured directory layouts
Bibliographic record
Abstract
🐛 Bug Fix Sanitize "locator" value #830 (@yarikoptic) [FIX] remove XA partial volumes, generated by dcm2niix as derived data #817 (@bpinsard) [ENH] filter dicoms earlier to avoid nibabel crash with XA ill-formed mutli-planar localizer dicoms #806 (@bpinsard) Address typing issues (and avoid some minor code duplication) #828 (@yarikoptic) remove bval bvecs files generated for SBRef on XA60/CMRR/dcm2niix #819 (@bpinsard) 🏠 Internal gh-actions: Bump actions/setup-python from 5 to 6 #833 (@dependabot[bot]) gh-actions: Bump actions/checkout from 4 to 5 #827 (@dependabot[bot]) Add claude workflow #827 (@yarikoptic) Use ubuntu-22.04 until we fixup NeuroDebian APT for debian and ubuntu #825 (@yarikoptic) 📝 Documentation Fix the name of the podman executable in the docs #829 (@mih) DOC: fix the description of filter_files #823 (@mslw @yarikoptic) 🧪 Tests Enable testing 3.13 #831 (@yarikoptic) Authors: 5 @dependabot[bot] Basile (@bpinsard) Michael Hanke (@mih) Michał Szczepanik (@mslw) Yaroslav Halchenko (@yarikoptic)
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.232 | 0.098 |
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".