Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.890 | 0.626 |
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 it