Bibliographic record
Abstract
Release Notes Bug-fix release in the 1.8.x series. This release includes compatibility fixes for nibabel 4.x and resolves a denial-of-service bug when the etelemetry server is down that resulted in excessive (blocking) network hits that would cause any tools using nipype interfaces to take a very long time. What's Changed FIX: Argument order to <code>numpy.save()</code> (https://github.com/nipy/nipype/pull/3485) FIX: Add tolerance parameter to ComputeDVARS (https://github.com/nipy/nipype/pull/3489) FIX: Delay access of nibabel.trackvis until actually needed (https://github.com/nipy/nipype/pull/3488) FIX: Avoid excessive etelemetry pings (https://github.com/nipy/nipype/pull/3484) ENH: Added outputs' generation to DWIBiascorrect interface (https://github.com/nipy/nipype/pull/3476) New Contributors @LostBenjamin made their first contribution in https://github.com/nipy/nipype/pull/3485 <strong>Full Changelog</strong>: https://github.com/nipy/nipype/compare/1.8.2...1.8.3
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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) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.485 | 0.613 |
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".