Notice bibliographique
Résumé
Release Notes This release changes many internal aspects of the code, will make future improvements easier, and will hopefully make it easier for more people to understand their results and contribute. The denoising results should be identical. Right before releasing this new version, we released version 0.0.13, which is the last version of the older code. If you want to confirm the consistency of results, these are the two versions you should compare. Instructions for comparing results are below. Key changes Large portions of the code were reorganized and modularized to make understanding the code easier and facilitate future development Breaking change: tedana can no longer be used to manually change component classifications. A separate program, ica_reclassify, can be used for this. This makes it easier for programs like Rica to output a list of component numbers to change and to then change them with ica_reclassify. The component classification process that designates components as "accepted" or "rejected" was completely rewritten so that every step in the process is modular and the inputs and outputs of every step are logged. The documentation includes descriptions of the newly outputted files and file contents. It is now possible to select different decision trees for component selection using the --tree option. The default tree is kundu and should replicate the current outputs. We also include minimal which is a simpler tree that is intended to provide more consistent results across a study, but still needs more testing and validation and may still change. Flow charts for these two options are here. Anyone can create their own decision tree. If one is using metrics that are already calculated, like kappa and rho, and doing greater/less than comparisons, one can make a decision tree with a user-provided json file. More complex calculations might require editing the tedana python code. This change also means any metric that has one value per component can be used in a selection process. This makes it possible to combine the multi-echo metrics used in tedana with other selection metrics, such as correlations to head motion. The documentation includes instructions on building and understanding this component selection process. Breaking change: No components are classified as ignored. "Ignored" has long confused users. It was intended to identify components with such low variation that it wasn't worth deciding whether to lose a statistical degree of freedom by rejecting them. They were treated identically to accepted components. Now they are classified as "accepted" and tagged as "Low variance" or "Borderline Accept". These classification tags now appear on the html report of the results. A registry of all files outputted by tedana is now stored with the outputs. This allows for multiple file naming methods and means internal and external programs that want to interact with the tedana outputs just need to load this file. Nearly 100% of the new code and 98% of all tedana code is covered by integration testing. Tedana python package management now uses pyproject.toml Minimum python version is now 3.8 and minimum pandas version is now 2.0 (might cause problems if the same python environment is used for packages that require older versions of pandas) More comprehensive documentation of changes is in pull request #756 and the full release notes are here: https://github.com/ME-ICA/tedana/releases/tag/23.0.0 Changes [REF] Decision Tree Modularization (#756) @jbteves @handwerkerd @n-reddy @marco7877 @tsalo
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,006 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,349 | 0,370 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».