nipy/nibabel: 3.1.0
Bibliographic record
Abstract
3.1.0 (Monday 20 April 2020) New feature release in the 3.1.x series. New features Conformation function (processing.conform) and CLI tool (nib-conform) to apply shape, orientation and zooms (pr/853) (Jakub Kaczmarzyk, reviewed by CM, YOH) Affine rescaling function (affines.rescale_affine) to update dimensions and voxel sizes (pr/853) (CM, reviewed by Jakub Kaczmarzyk) Bug fixes Delay import of h5py until neded (pr/889) (YOH, reviewed by CM) Maintenance Fix typo in documentation (pr/893) (Zvi Baratz, reviewed by CM) Tests converted from nose to pytest (pr/865 + many sub-PRs) (Dorota Jarecka, Krzyzstof Gorgolewski, Roberto Guidotti, Anibal Solon, Or Duek, CM) API changes and deprecations kw_only_meth/kw_only_func decorators are deprecated (pr/848) (RM, 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.414 | 0.599 |
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".