Bibliographic record
Abstract
Release Notes Major changes: This release reverts to the 2.5 version selection criteria, and it also switches the ICA implementation from mdp to sklearn. It is also includes a major overhaul of the documentation. With thanks to @frodeaa, @RupeshGoud, and @jbteves for contributrions ! Changes [DOC] Rearrange badges in README (#118) @tsalo [ENH] Linting, update imports (#4) @emdupre [FIX] Add quiet and debug options to t2smap (#123) @emdupre [DOC] Add Python version info (#126) @tsalo [FIX] Accept non-NIFTI files without complaining (#128) @rmarkello [FIX] Remove nifti requirement in selcomps() (#130) @rmarkello Inital commit of tedana package (#1) @emdupre [DOC] Update multi-echo.rst (#138) @RupeshGoud [FIX] Logging in tedana and t2smap (#143) @frodeaa [ENH] Track PCA and ICA component selection decisions (#122) @tsalo [DOC] Improve documentation for pipeline (#133) @tsalo Documentation update for installation and environments in miniconda (#142) @jbteves [DOC] Add Support page (#150) @tsalo [ENH] Rename modules (#136) @frodeaa [DOC] Update documentation for interacting with other pipelines (#134) @emdupre Merge in @rmarkello PR (#19) @emdupre [TST] Support Python 3.5 (#154) @tsalo [DOC] Request for Comments: Roadmap and Contributing (#151) @emdupre [ENH] update ICA to sklearn from mdp (#44) @emdupre [DOC] RST formatting fixes for roadmap, contributing (#157) @emdupre [ENH] Switch to Selcomps 2.5 (#119) @emdupre [FIX] Loop through volumes in FIT method (#158) @tsalo
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.493 | 0.589 |
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".