Bibliographic record
Abstract
This release contains a number of breaking, fixing, and useful changes. We encourage users to review our heavily expanded documentation at tedana.readthedocs.io Bug Fixes: PCA has been overhauled to a new and more reliable method, averting a known bug where too many PCA components would be selected. Environments are not coerced to single-threaded computation after calling tedana. Fixed variance-explained outlier detection problem where first value was always NaN and variance explained was always negative. Fixed component table loading bug that resulted from unexpected pandas behavior. Fixed bug where the wrong number of echoes would be allocated in-program. Fixed bug where only selecting one component would cause an error. Correctly incorporate user-supplied masks in T2* workflow. Fixed bug in PAID combination where mean of data would be used instead of SNR. Breaking Changes: Log files are now by datetime, allowing multiple runs to have systematic naming. Filenames for decomposition and metric maps are now BIDS derivative-compatible. Please see documentation for the full list of new filenames. Component tables are now in .json format Changed tab-separated files from .txt to .tsv file extension. Removed the --sourceTEs option. T2* maps are now in seconds rather than milliseconds. --mle option is now deprecated. Changes in Defaults: New PCA algorithm is default, please see documentation for more information. Clustering is now bi-sided rather than two sided (positive and negative clusters are now grouped separately). Static png images are now the default; use --nopng to avoid this. Files are now gzipped by default. New Features: Massively expanded documentation, please see tedana.readthedocs.io to view the updated usage help, multi-echo background, developer guidelines, and API documentation. New PCA decomposition algorithm (default). Adds the --out_dir argument to t2smap workflow to choose what directory files are written to. t2smap workflow is now fmriprep compatible Added --t2smap argument to allow you to supply a precalculated T2* map. Thanks to Logan Dowdle, Elizabeth DuPre, Cesar Caballero Gaudes, Dan Handwerker, Ross Markello, Isla, Joshua Teves, Eneko Urunuela, Kirstie Whitaker, and to the NIH Section on Functional Imaging Methods for supporting the tedana hackathon and the NIH for supporting the AFNI Code Convergence, where much of the work in this release was done.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.546 | 0.540 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".