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 <code>pip install --pre nibabel</code>. New features ArrayProxy method <code>get_scaled()</code> scales data with a dtype of a specified precision, promoting as necessary to avoid overflow. This is to used in <code>img.get_fdata()</code> to control memory usage. (pr/833) (CM, reviewed by Ross Markello) GiftiImage method <code>agg_data()</code> to return usable data arrays (pr/793) (Hao-Ting Wang, reviewed by CM) Accept <code>os.PathLike</code> 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 <code>get_fdata(dtype=np.float32)</code> will attempt to avoid casting data to <code>np.float64</code> when scaling parameters would otherwise promote the data type unnecessarily. (pr/833) (CM, reviewed by Ross Markello) <code>ArraySequence</code> 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 <code>Tractogram</code>s no longer <code>apply_affine</code> to the original <code>Tractogram</code>'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 <code>ArraySequence.data</code> in favor of <code>ArraySequence.get_data()</code>, which will return a copy. <code>ArraySequence.data</code> now returns a read-only view. (pr/811) (MC, reviewed by Serge Koudoro, Philippe Poulin, CM, MB) Deprecate <code>DataobjImage.get_data()</code> API, to be removed in nibabel 5.0 (pr/794, pr/809) (CM, reviewed by MB)
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.110 | 0.005 |
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; both teacher heads agree on what is shown here.
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".