physiopy/phys2bids: BIDS formatting of physiological recordings
Bibliographic record
Abstract
:tada: This release contains work from a new contributor! :tada: Thank you, Stefano Moia (@smoia), for all your work! 🐛 Bug Fix Rename "run" into "take" for clearer purposes and fix naming convention in multi-take overlapping #427 (@smoia) Fix trigger plots when trigger has different sampling than time #426 (@smoia) Prevent slicing from terminating program if end of last slice is above maximum timepoints. #425 (@smoia) Update trigger threshold guess estimation to mean of trigger channel #377 (@62442katieb) 📝 Documentation Update contributors list #423 (s.moia@bcbl.eu @smoia) ⚠️ Tests Fix testing enviroment by using apt-get install build-essential rather than apt-get install make #413 (@vinferrer) 🏠 Internal Add @drombas as contributor #416 (@smoia) Authors: 4 Katie Bottenhorn (@62442katieb) smoia (s.moia@bcbl.eu) Stefano Moia (@smoia) Vicente Ferrer (@vinferrer)
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.463 | 0.341 |
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".