nipy/nibabel: 3.2.0
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, Stephan Gerhard and Ross Markello (RM). References like "pr/298" refer to github pull request numbers. 3.2.0 (Tuesday 20 October 2020) New feature release in the 3.2.x series. New features nib-stats CLI tool to expose new nibabel.imagestats API. Initial implementation of volume calculations, a la fslstats -V. (pr/952) (Julian Klug, reviewed by CM and GitHub user 0rC0) nib-roi CLI tool to crop images and/or flip axes (pr/947) (CM, reviewed by Chris Cheng and Mathias Goncalves) Parser for Siemens "ASCCONV" text format (pr/896) (Brendan Moloney and MB, reviewed by CM) Enhancements Drop confusing mention of img.to_filename() in getting started guide (pr/946) (Fernando Pérez-Garcia, reviewed by MB, CM) Implement to_bytes()/from_bytes() methods for Cifti2Image (pr/938) (CM, reviewed by Mathias Goncalves) Clean up of DICOM documentation (pr/910) (Jonathan Daniel, reviewed by MB) Bug fixes Use canvas manager API to set title in OrthoSlicer3D (pr/958) (EL, reviewed by CM) Record units as seconds parrec2nii; previously set TR to seconds but retained msec units (pr/931) (CM, reviewed by MB) Reflect on-disk dimensions in NIfTI-2 view of CIFTI-2 images (pr/930) (Mathias Goncalves and CM) Fix outdated Python 2 and Sympy code in DICOM derivations (pr/911) (MB, reviewed by CM) Change string with invalid escape to raw string (pr/909) (EL, reviewed by MB) Maintenance Fix typo in docs (pr/955) (Carl Gauthier, reviewed by CM) Purge nose from nisext tests (pr/934) (Markéta Calábková, reviewed by CM) Suppress expected warnings in tests (pr/949) (CM, reviewed by Dorota Jarecka) Various cleanups and modernizations (pr/916, pr/917, pr/918, pr/919) (Jonathan Daniel, reviewed by CM) SVG logo for improved appearance in with zooming (pr/914) (Jonathan Daniel, reviewed by CM) API changes and deprecations Drop support for Numpy < 1.13 (pr/922) (CM) Warn on use of onetime.setattr_on_read, which has been a deprecated alias of auto_attr (pr/948) (CM, reviewed by Ariel Rokem)
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.413 | 0.519 |
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".