poldracklab/fmriprep: 1.2.6
Bibliographic record
Abstract
Release Notes This is a bug fix release in the 1.2 series. Probably the most noticeable improvement is the restoration of auto-generated content in the documentation. Additionally, FreeSurfer aparc/aseg segmentations are now sampled to all output spaces. For any users importing fMRIPrep interfaces, many of these have been moved to the niworkflows package. With thanks to Nir Jacoby and Hrvoje Stojic for contributions. CHANGES [FIX] Use keyword arguments for Sentry breadcrumb reporting (#1441) @chrisfilo [FIX] Verify proc file exists before reading (#1454) @effigies [ENH] Only report participants with errors (#1437) @effigies [ENH] Resample aparc/aseg into specified output spaces (#1401) @nirjacoby [ENH] Copy BibTeX file to log directory for LaTeX users (#1446) @hstojic [RF] Use niworkflows upstreamed interfaces and utilities (#1438) @oesteban [DOC] Fix documentation build (#1451) @oesteban [DOC] Fix ReadTheDocs builds (#1459) @effigies [MAINT/DOC] Clean-up __about__, update with Nat Meth (#1445) @oesteban [MAINT] Make sure Python 3.7.1 is installed (#1452) @oesteban [MAINT] Dev status to beta, bump copyright year (#1468) @effigies
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.573 | 0.554 |
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".