Bibliographic record
Abstract
1.0.3 (April 30, 2018) Full changelog FIX: Propagate explicit Workflow config to Nodes (https://github.com/nipy/nipype/pull/2559) FIX: Return non-enhanced volumes from dwi_flirt (https://github.com/nipy/nipype/pull/2547) FIX: Skip filename generation when required fields are missing (https://github.com/nipy/nipype/pull/2549) FIX: Fix Afni's Allineate hashing and out_file (https://github.com/nipy/nipype/pull/2502) FIX: Replace deprecated HasTraits.get with trait_get (https://github.com/nipy/nipype/pull/2534) FIX: Typo in "antsRegistrationSyNQuick.sh" (https://github.com/nipy/nipype/pull/2544) FIX: DTITK Interface (https://github.com/nipy/nipype/pull/2514) FIX: Add -mas argument to fsl.utils.ImageMaths (https://github.com/nipy/nipype/pull/2529) FIX: Build cmdline from working directory (https://github.com/nipy/nipype/pull/2521) FIX: FSL orthogonalization bug (https://github.com/nipy/nipype/pull/2523) FIX: Re-enable dcm2niix source_names (https://github.com/nipy/nipype/pull/2550) ENH: Add an activation count map interface (https://github.com/nipy/nipype/pull/2522) ENH: Revise the implementation of FuzzyOverlap (https://github.com/nipy/nipype/pull/2530) ENH: Add MultiObject, ensure/simplify_list; alias old names for 1.x compatibility (https://github.com/nipy/nipype/pull/2517) ENH: Add LibraryBaseInterface (https://github.com/nipy/nipype/pull/2538) ENH: Define default output file template for afni.CatMatvec (https://github.com/nipy/nipype/pull/2527) MAINT: Deprecate terminal_output and ignore_exception from CommandLine (https://github.com/nipy/nipype/pull/2552) MAINT: Set traits default values properly (https://github.com/nipy/nipype/pull/2533) MAINT: use RawConfigParser (https://github.com/nipy/nipype/pull/2542) MAINT: Minor autotest cleanups (https://github.com/nipy/nipype/pull/2519) CI: Add retry script for Docker commands (https://github.com/nipy/nipype/pull/2516)
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) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.520 | 0.618 |
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".