Bibliographic record
Abstract
<!-- Release notes generated using configuration in .github/release.yml at main --> What's Changed 🐛 Bug Fixes Convert 3D AFNI volumes to actual 3D by @handwerkerd in https://github.com/ME-ICA/tedana/pull/1381 nibabel opens AFNI formatted 3D volumes so that there is a 4th dimension of length 1. The nilearn.plotting function add_contours crashes when it expects a 3D and receives a 4D volume. This resulted in tedana crashing when it is run as part of the afni_proc.py pipeline. The main reason we are releasing this version is to address this issue. Other Changes Organize CLI arguments into groups by @tsalo in https://github.com/ME-ICA/tedana/pull/1369 The help text in tedana --help and for ica_reclassify and ts2map are better organized with some edits for clarity. Check that classification tags are used in decision trees and that each decision tree includes the Likely BOLD tag by @tsalo in https://github.com/ME-ICA/tedana/pull/1370 Better checking to make sure a user-provided decision tree will operate well within tedana. A few things that used to raise warnings will now raise errors. This may cause issues if someone was using decision trees that previously raised warnings. Address test and RTD build warnings by @tsalo in https://github.com/ME-ICA/tedana/pull/1376 Some parts of the documentation at https://tedana.readthedocs.io should be properly rendered now and benign warnings were fixed. fix: Update open_rica_report.py with current Rica file patterns by @eurunuela in https://github.com/ME-ICA/tedana/pull/1375 Makes sure tedana interacts well with new updated to https://github.com/me-ICA/rica Full Changelog: https://github.com/ME-ICA/tedana/compare/26.0.0...26.0.1
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.535 | 0.733 |
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".