Bibliographic record
Abstract
Release Notes 🐛 Bug Fix and (un)breaking change Fixing unflipped PCA component weights by @handwerkerd in https://github.com/ME-ICA/tedana/pull/1234 #1212 was released with v25.0.0 and included an error where the PCA mixing matrix time series, but not the weight maps were multiplied by -1. This altered the ICA results in undocumented and probably improper ways. This PR fixes that bug. For identical parameters, running v24.0.02 and v25.0.1 in the same environment should result in nearly identical ICA mixing matrices (except for some time series being multipled by -1). People using v25.0.0 should update. Breaking Change Replace --mir with --gscontrol mir in ica_reclassify by @tsalo in https://github.com/ME-ICA/tedana/pull/1222 Changed so a parameter option in ica_reclassify more closely matches the naming scheme in tedana Enhancements Add gscontrol figures by @tsalo in https://github.com/ME-ICA/tedana/pull/1221 Add external regressor-mixing matrix correlation heat map by @tsalo in https://github.com/ME-ICA/tedana/pull/1226 The above two PRs add new figures to the report to help users between understand and quality check results if they use external regressors or global signal regression. Add --dummy-scans parameter to CLIs by @tsalo in https://github.com/ME-ICA/tedana/pull/1232 tedana now has a parameter --dummy-scans that can be used to remove the first X volumes of fMRI data from within tedana. Project maintenance Drop Python 3.8 support by @effigies in https://github.com/ME-ICA/tedana/pull/1228 Python 3.8 is considered end-of-life and it was starting to cause some backwards compatibility issues. Full Changelog: https://github.com/ME-ICA/tedana/compare/25.0.0...25.0.1
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.319 | 0.428 |
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".