Bibliographic record
Abstract
New feature release in the 5.0.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). New features SerializableImage now has to_stream() and from_stream() methods to read/write streams implementing the io.IOBase interface. A from_url() method enables loading images from URLs. (pr/1129) (CM, reviewed by MB) TrkFile supports TRKv3, an undocumented but generally compatible variant of TRKv2. (pr/1125) (CM) Enhancements Support multiline header fields in TCKFile (pr/1175) (CM, reviewed by Matt Cieslak) Make layout order an initialization parameter of ArrayProxy (pr/1131) (CM, reviewed by MB) Initial support for type annotations. (pr/1115, pr/1178) (CM, reviewed by Zvi Baratz) Bug fixes Handle extension/file-format mismatches implemented incompletely in pr/1013 (pr/1138) (CM, reviewed by Thomas Phil) Improve handling of invalid TCK files, which could sometimes cause an infinite loop (pr/1140) (Anibal Solon, reviewed by CM) Clean up ECAT test case that left filehandle open and failed to use class variables (pr/1155) (Dimitri Papadopoulos, reviewed by CM) Maintenance Simplify TCK reading code by assuming files are open in binary mode (pr/1142) (Anibal Solon, reviewed by MC, CM) Code support for tests covering deprecated functionality (pr/1159) (CM) Miscellaneous code cleanups (pr/1148, pr/1149, pr/1153, pr/1154, pr/1156) (Dimitri Papadopoulos, reviewed by CM) Update CI to build, test and deploy PyPI artifacts (pr/1134) (CM, reviewed by MB) Transition from setup.cfg to pyproject.toml package configuration (pr/1133) (CM, reviewed by MB) Addressed race conditions preventing running tests with pytest-xdist. (pr/1157, pr/1158) (CM, reviewed by Christian Haselgrove) Apply blue and isort auto-formatters and provide pre-commit configuration to reduce human burden of style guidelines. (pr/1124, pr/1165, pr/1169) (CM and Zvi Baratz) Manage versioning with setuptools_scm (pr/1171) (CM, reviewed by Zvi Baratz) Reduce installed package size by excluding very large test file (pr/1176) (CM, reviewed by Zvi Baratz) API changes and deprecations Passing an int64 array to Nifti1Image without a header or dtype argument will raise a ValueError. (pr/1173) (CM) tmpdirs.TemporaryDirectory is deprecated in favor of tempfile.TemporaryDirectory. (pr/1172) (CM, reviewed by Zvi Baratz) The nisext package is deprecated and will be removed in NiBabel 6.0. (pr/1170) (CM, reviewed by MB) Drop support for Python 3.7, Numpy < 1.19 (pr/1177) (CM) The following deprecated functions and methods will now raise ExpiredDeprecationErrors nibabel.loadsave.read_img_data nibabel.dataobj_images.DataobjImage.get_data nibabel.loadsave.guessed_image_type nibabel.onetime.setattr_on_read nibabel.orientations.flip_axis Modules, classes and functions that expired at 4.0 were fully removed. ExpiredDeprecationError\s will now be AttributeError\s.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.270 | 0.445 |
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".