Bibliographic record
Abstract
1.7.0 (October 20, 2021) New feature release in the 1.7.x series. (Full changelog FIX: Make ants.LaplacianThickness output_image a string, not file (https://github.com/nipy/nipype/pull/3393) FIX: coord for mrconvert (https://github.com/nipy/nipype/pull/3369) FIX: antsRegistration allows the restrict_deformation to be float (https://github.com/nipy/nipype/pull/3387) FIX: Also allow errno.EBUSY during emptydirs on NFS (https://github.com/nipy/nipype/pull/3357) FIX: Removed exists=True from MathsOutput (https://github.com/nipy/nipype/pull/3385) FIX: Extension not extensions, after pybids v0.9 (https://github.com/nipy/nipype/pull/3380) ENH: Add CAT12 SANLM denoising filter (https://github.com/nipy/nipype/pull/3374) ENH: Add expected steps for FreeSurfer 7 recon-all (https://github.com/nipy/nipype/pull/3389) ENH: Stop printing false positive differences when logging cached nodes (https://github.com/nipy/nipype/pull/3376) ENH: Add new flags to MRtrix/preprocess.py (DWI2Tensor, MRtransform) (https://github.com/nipy/nipype/pull/3365) ENH: verbose input should not be hashed in ants.Registration (https://github.com/nipy/nipype/pull/3377) REF: Clean-up the BaseInterface run() function using context (https://github.com/nipy/nipype/pull/3347) DOC: Fix typo in README (https://github.com/nipy/nipype/pull/3386) STY: Make private member name consistent with the rest of them (https://github.com/nipy/nipype/pull/3346) MNT: Simplify interface execution and better error handling of Node (https://github.com/nipy/nipype/pull/3349) MNT: Add user name and email to Docker to appease git/annex/datalad (https://github.com/nipy/nipype/pull/3378) CI: Update CircleCI machine image (https://github.com/nipy/nipype/pull/3391)
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.638 | 0.790 |
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".