Bibliographic record
Abstract
1.3.0-rc1 (October 8, 2019) Release candidate for Nipype 1.3.0, released off-schedule to enable testing and allow downstream projects to move off of 1.2.3. Final release scheduled for Monday, October 28. Full changelog FIX: Restore AFNICommand._get_fname, required by some interfaces (https://github.com/nipy/nipype/pull/3071) FIX: Remove asynchronous chdir callback (https://github.com/nipy/nipype/pull/3060) FIX: Minimize scope for directory changes while loading results file (https://github.com/nipy/nipype/pull/3061) ENH: Add precommit information for contributors and pre-commit style (https://github.com/nipy/nipype/pull/3063) ENH: Delay etelemetry for non-interactive sessions, report bad versions (https://github.com/nipy/nipype/pull/3049) ENH: Run memoized check_version at REPL import, Node/Workflow/Interface init (https://github.com/nipy/nipype/pull/30) RF: Provide functions to augment old Path.mkdir, Path.resolve methods (https://github.com/nipy/nipype/pull/3050) RF: Redirect nipype.workflows to niflow.nipype1.workflows (https://github.com/nipy/nipype/pull/3067) TST: Skip dcm2niix test if data fails to download (https://github.com/nipy/nipype/pull/3059) TST: dcm2niix test fix (https://github.com/nipy/nipype/pull/3058) MAINT: Drop Python 3.4 support (https://github.com/nipy/nipype/pull/3062) CI: make specs on Travis (https://github.com/nipy/nipype/pull/3066)
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.592 | 0.769 |
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".