Bibliographic record
Abstract
Summary This would ordinarily not have been released, but an issue with one of our dependencies means that people cannot install tedana right now. The most notable change (which will potentially change your results!) is that PCA is now defaulting to the "aic" criterion rather than the "mdl" criterion. What's Changed [DOC] Add JOSS badges by @tsalo in https://github.com/ME-ICA/tedana/pull/815 [FIX] Fixes broken component figures in report when there are more than 99 components by @manfredg in https://github.com/ME-ICA/tedana/pull/824 [DOC] Add manfredg as a contributor for code by @allcontributors in https://github.com/ME-ICA/tedana/pull/825 DOC: Use RST link for ME-ICA by @effigies in https://github.com/ME-ICA/tedana/pull/832 [DOC] Fixing a bunch of warnings & rendering issues in the documentation by @handwerkerd in https://github.com/ME-ICA/tedana/pull/840 [DOC] Replace mentions of Gitter with Mattermost by @tsalo in https://github.com/ME-ICA/tedana/pull/842 [FIX] The rationale column of comptable gets updated when no manacc is given by @eurunuela in https://github.com/ME-ICA/tedana/pull/855 Made AIC the default maPCA option by @eurunuela in https://github.com/ME-ICA/tedana/pull/849 [DOC] Improve logging of component table-based manual classification by @tsalo in https://github.com/ME-ICA/tedana/pull/852 [FIX] Add jinja2 version pin as workaround by @jbteves in https://github.com/ME-ICA/tedana/pull/870 New Contributors @manfredg made their first contribution in https://github.com/ME-ICA/tedana/pull/824 Full Changelog: https://github.com/ME-ICA/tedana/compare/0.0.11...0.0.12
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.631 | 0.706 |
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".