Bibliographic record
Abstract
New feature release in the 5.1.x series. Most work on NiBabel so far has been by Matthew Brett (MB), Chris Markiewicz (CM), Michael Hanke (MH), Marc-Alexandre Côté (MC), Ben Cipollini (BC), Paul McCarthy (PM), Chris Cheng (CC), Yaroslav Halchenko (YOH), Satra Ghosh (SG), Eric Larson (EL), Demian Wassermann, Stephan Gerhard and Ross Markello (RM). References like "pr/298" refer to github pull request numbers. Enhancements Make imagestats available with import nibabel (pr/1208) (Fabian Perez, reviewed by CM) Use symmetric threshold for identifying unit quaternions on qform calculations (pr/1182) (CM, reviewed by MB) Type annotations for nibabel.loadsave (pr/1213) and nibabel.spatialimages.SpatialImage APIs (pr/1179), nibabel.deprecated, nibabel.deprecator, nibabel.onetime and nibabel.optpkg modules (pr/1188), nibabel.volumeutils (pr/1189), nibabel.filename_parser and nibabel.openers (pr/1197) (CM, reviewed by Zvi Baratz) Bug fixes Require explicit overrides to write GIFTI files that contain data arrays with data types not permitted by the GIFTI standard (pr/1199) (CM, reviewed by Alexis Thual) Maintenance Move compression detection logic into a private nibabel._compression module, resolving unexpected errors from pyzstd. (pr/1212) (CM) Improved consistency of docstring formatting (pr/1200) (Zvi Baratz, reviewed by CM) Modernized README text (pr/1195) (Zvi Baratz, reviewed by CM) Updated README badges to include package distributions (pr/1192) (Horea Christian, reviewed by CM) Removed all dependencies on distutils and setuptools (pr/1190) (CM, reviewed by Zvi Baratz) Add a _version.pyi stub to allow mypy to run without building nibabel (pr/1210) (CM) New Contributors @TheChymera made their first contribution in https://github.com/nipy/nibabel/pull/1192 @Factral made their first contribution in https://github.com/nipy/nibabel/pull/1208 Full Changelog: https://github.com/nipy/nibabel/compare/5.0.1...5.1.0
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.419 | 0.565 |
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".