Bibliographic record
Abstract
Release Notes A long overdue release. This release integrates the great work of @mgxd to provide results in the CIFTI/grayordinates format for surface-based analyses. It also fixes several issues related to ICA-AROMA, and a long list of miscellaneous improvements. CHANGES [MAINT] fmriprep-docker: Ensure data/output/work paths are absolute (#1089) [ENH] Add usage tracking and centralized error reporting (#1088) [FIX] Ensure one motion IC index is loaded as list (#1096) [TST] Refactoring CircleCI setup (#1098) [FIX] Compression in DataSinks (#1095) [MAINT] fmriprep-docker: Support Python 2/3 without future or other helpers (#1082) [MAINT] Update npm to 10.x (#1087) [DOC] Prefer pre-print over Zenodo doi in boilerplate (#1086) [DOC] Stylistic fix (`'template'`) (#1083) [FIX] Run ICA-AROMA in MNI152Lin 2mm resampling grid (91x109x91 vox) (#1064) [MAINT] Remove cwebp to revert to png (#1081) [ENH] Allow changing the dimensionality of Melodic for AROMA. (#1052) [FIX] Derivatives datasink handling of compression (#1077) [FIX] Check for invalid sform matrices (#1072) [FIX] Check exit code from subprocess (#1073) [DOC] Add preprint fig. 1 to About (#1070) [FIX] Always strip session from T1w for derivative naming (#1071) [DOC] Add RRIDs in the citation boilerplate (#1061) [ENH] Generate CIFTI derivatives (#1001)
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.622 | 0.583 |
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".