nipy/nibabel: 3.0.0rc1
Bibliographic record
Abstract
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. 3.0.0rc1 (Saturday 16 November 2019) Release candidate for NiBabel 3.0, initiating a minimum one-month testing window. Downstream projects are requested to test against the release candidate by installing with pip install --pre nibabel. New features ArrayProxy method get_scaled() scales data with a dtype of a specified precision, promoting as necessary to avoid overflow. This is to used in img.get_fdata() to control memory usage. (pr/833) (CM, reviewed by Ross Markello) GiftiImage method agg_data() to return usable data arrays (pr/793) (Hao-Ting Wang, reviewed by CM) Accept os.PathLike objects in place of filenames (pr/610) (Cameron Riddell, reviewed by MB, CM) Function to calculate obliquity of affines (pr/815) (Oscar Esteban, reviewed by MB) Enhancements get_fdata(dtype=np.float32) will attempt to avoid casting data to np.float64 when scaling parameters would otherwise promote the data type unnecessarily. (pr/833) (CM, reviewed by Ross Markello) ArraySequence now supports a large set of Python operators to combine or update in-place. (pr/811) (MC, reviewed by Serge Koudoro, Philippe Poulin, CM, MB) Warn, rather than fail, on DICOMs with unreadable Siemens CSA tags (pr/818) (Henry Braun, reviewed by CM) Improve clarity of coordinate system tutorial (pr/823) (Egor Panfilov, reviewed by MB) Bug fixes Sliced Tractograms no longer apply_affine to the original Tractogram's streamlines. (pr/811) (MC, reviewed by Serge Koudoro, Philippe Poulin, CM, MB) Re-import externals/netcdf.py from scipy to resolve numpy deprecation (pr/821) (CM) Maintenance Support Python >=3.5.1, including Python 3.8.0 (pr/787) (CM) Manage versioning with slightly customized Versioneer (pr/786) (CM) Reference Nipy Community Code and Nibabel Developer Guidelines in GitHub community documents (pr/778) (CM, reviewed by MB) API changes and deprecations Deprecate ArraySequence.data in favor of ArraySequence.get_data(), which will return a copy. ArraySequence.data now returns a read-only view. (pr/811) (MC, reviewed by Serge Koudoro, Philippe Poulin, CM, MB) Deprecate DataobjImage.get_data() API, to be removed in nibabel 5.0 (pr/794, pr/809) (CM, reviewed by MB)
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.490 | 0.712 |
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".