ME-ICA/tedana: 0.0.6
Bibliographic record
Abstract
Release Notes We had several major changes this release, including: Changes PCA default component selection to MLE, with previous decision tree accessible through kundu_pca argument Adds verbose outputs for visualization and debugging Addition of tedort argument Bug fix for user-defined mask with poor signal Improved documentation, logging, and issue templates also added. With thanks to @dowdlelt, @jbteves, @KirstieJane, and @tsalo ! Changes Hyperlink DOIs to preferred resolver (#165) @katrinleinweber [REF] Replace hard-coded F-statistic thresholds with scipy.stats function call (#156) @tsalo [FIX] Include ignored components in ME-DN T1c time series (#125) @tsalo [REF] Remove unused arguments and simplify CLI (#163) @tsalo [DOC] Add FAQ and link to ME papers spreadsheet (#160) @tsalo [DOC] Improve logging (#167) @tsalo [FIX] Reduce user-defined mask when there is no good signal (#172) @tsalo [ENH] Add tedort argument to tedana workflow (#155) @tsalo [ENH] Split automatic dimensionality detection from decision tree in TEDPCA (#164) @tsalo [ENH] Add verbose outputs for pipeline walkthrough (#174) @tsalo [fix] update python version support in README (#182) @emdupre [DOC] Fix eimask logging, ste definitions in eigendecomp (#184) @dowdlelt [DOC] Fix arg parser (#195) @dowdlelt Fix broken link to code of conduct (#198) @KirstieJane [DOC] Add tedana development setup instructions (#197) @jbteves Corrects README.md to show correct conda and pip instructions (#205) @jbteves [FIX] Propagate TR to ref_image header (#207) @dowdlelt [FIX] Do not use minimum mask for OC data in tedpca (#204) @tsalo [ENH] Adds issue templates for bugs and discussions (#189) @jbteves [ENH] Normalize all the line endings (#191) @jbteves
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.419 | 0.363 |
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".