nipy/nibabel: 2.4.1
Bibliographic record
Abstract
Bug fix release for the 2.4.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, and Stephan Gerhard. References like "pr/298" refer to github pull request numbers. 2.4.1 (Monday 27 May 2019) Contributions from Egor Pafilov, Jath Palasubramaniam, Richard Nemec, and Dave Allured. Enhancements Enable mmap, keep_file_open options when loading any DataobjImage (pr/759) (CM, reviewed by PM) Bug fixes Ensure loaded GIFTI files expose writable data arrays (pr/750) (CM, reviewed by PM) Safer warning registry manipulation when checking for overflows (pr/753) (CM, reviewed by MB) Correctly write .annot files with duplicate lables (pr/763) (Richard Nemec with CM) Maintenance Fix typo in coordinate systems doc (pr/751) (Egor Panfilov, reviewed by CM) Replace invalid MINC1 test file with fixed file (pr/754) (Dave Allured with CM) Update Sphinx config to support recent Sphinx/numpydoc (pr/749) (CM, reviewed by PM) Pacify FutureWarning and DeprecationWarning from h5py, numpy (pr/760) (CM) Accommodate Python 3.8 deprecation of collections.MutableMapping (pr/762) (Jath Palasubramaniam, reviewed by CM) API changes and deprecations Deprecate keep_file_open == 'auto' (pr/761) (CM, reviewed by PM)
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.251 | 0.394 |
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".