nipy/nibabel: 3.0.0rc2
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. 3.0.0rc2 (Wednesday 11 December 2019) New features ArrayProxy __array__() now accepts a dtype parameter, allowing numpy.array(dataobj, dtype=...) calls, as well as casting directly with a dtype (for example, numpy.float32(dataobj)) to control the output type. Scale factors (slope, intercept) are applied, but may be cast to narrower types, to control memory usage. This is now the basis of img.get_fdata(), which will scale data in single precision if the output type is float32. (pr/844) (CM, reviewed by Alejandro de la Vega, 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 Improve testing of data scaling in ArrayProxy API (pr/847) (CM, reviewed by Alejandro de la Vega) Document SpatialImage.slicer interface (pr/846) (CM) 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 Remove replicated metadata for packaged data from MANIFEST.in (pr/845) (CM) 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 Fully remove deprecated checkwarns and minc modules. (pr/852) (CM) The keep_file_open argument to file load operations and ArrayProxys no longer acccepts the value "auto", raising a ValueError. (pr/852) (CM) 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)
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 | 0.003 | 0.006 |
| 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 | 0.007 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.483 | 0.648 |
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".