Bibliographic record
Abstract
1.2.0 (May 09, 2019) Full changelog FIX: Parsing of filename in AlignEpiAnatPy when filename does not have + (https://github.com/nipy/nipype/pull/2909) FIX: Import nibabel reorientation bug fix (https://github.com/nipy/nipype/pull/2912) FIX: Update FNIRT outputs for warped_file log_file to include cwd (https://github.com/nipy/nipype/pull/2900) FIX: Sort conditions in bids_gen_info to ensure consistent order (https://github.com/nipy/nipype/pull/2867) FIX: Some traits-5.0.0 don't work with Python 2.7 (https://github.com/nipy/nipype/pull/1) ENH: CompCor enhancement (https://github.com/nipy/nipype/pull/2878) ENH: Do not override caught exceptions with FileNotFoundError from unfinished hashfile (https://github.com/nipy/nipype/pull/2919) ENH: More verbose description when a faulty results file is loaded (https://github.com/nipy/nipype/pull/2920) ENH: Add all DIPY workflows dynamically (https://github.com/nipy/nipype/pull/2905) ENH: Add mrdegibbs and dwibiascorrect from mrtrix3 (https://github.com/nipy/nipype/pull/2904) TEST: Fix CI builds (https://github.com/nipy/nipype/pull/2927) MAINT: Reduce deprecation warnings (https://github.com/nipy/nipype/pull/2903)
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 | 0.013 |
| 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 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.481 | 0.700 |
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".