Bibliographic record
Abstract
Bug fix release for the 2.5.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. 2.5.1 (Monday 23 September 2019) The 2.5.x series is the last with support for either Python 2 or Python 3.4. Extended support for this series 2.5 will last through December 2020. Enhancements Ignore endianness in nib-diff if values match (pr/799) (YOH, reviewed by CM) Bug fixes Correctly handle Philips DICOMs w/ derived volume (pr/795) (Mathias Goncalves, reviewed by CM) Raise CSA tag limit to 1000, parametrize for future relaxing (pr/798, backported to 2.5.x in pr/800) (Henry Braun, reviewed by CM, MB) Coerce data types to match NIfTI intent codes when writing GIFTI data arrays (pr/806) (CM, reported by Tom Holroyd) Maintenance Require h5py 2.10 for Windows + Python < 3.6 to resolve unexpected dtypes in Minc2 data (pr/804) (CM, reviewed by YOH) API changes and deprecations Deprecate nicom.dicomwrappers.Wrapper.get_affine() in favor of affine property; final removal in nibabel 4.0 (pr/796) (YOH, reviewed by CM)
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 | 0.004 | 0.014 |
| 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 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.230 | 0.384 |
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".