Notice bibliographique
Résumé
<!-- Release notes generated using configuration in .github/release.yml at main --> This release continues ongoing work on improving memory usage. We have eliminated the memory_limit option in our Estimators in favor of using sparse arrays. We expect to see a corresponding increase in fit times, especially for Monte Carlo FWE correction- however, we plan to address this in future releases. What's Changed 🛠 Breaking Changes Replace multiprocessing with joblib for parallelization and change n_cores default to 1 by @tsalo in https://github.com/neurostuff/NiMARE/pull/597 Incorporate joblib into ALESubtraction and fix SCALE docstring by @tsalo in https://github.com/neurostuff/NiMARE/pull/641 Stop storing MetaResults as attributes of fitted Estimators by @tsalo in https://github.com/neurostuff/NiMARE/pull/657 Refactor Correctors and remove statsmodels requirement by @tsalo in https://github.com/neurostuff/NiMARE/pull/679 ### 🎉 Exciting New Features Add FocusCounter diagnostic tool by @tsalo in https://github.com/neurostuff/NiMARE/pull/649 Support cluster-level Monte Carlo FWE correction in the MKDAChi2 Estimator by @tsalo in https://github.com/neurostuff/NiMARE/pull/650 Support vfwe_only in CBMAEstimator even when null_method isn't montecarlo by @tsalo in https://github.com/neurostuff/NiMARE/pull/678 Add warning when coordinates dataset contains both positive and negative z_stats by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/699 Add parameter estimate standard error to IBMA results by @tsalo in https://github.com/neurostuff/NiMARE/pull/691 Use sparse array in ALE, ALESubtraction, SCALE, KDA, and MKDADensity by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/725 ### 🐛 Bug Fixes Retain updated Estimator in Corrector-generated MetaResults by @tsalo in https://github.com/neurostuff/NiMARE/pull/633 Do not inherit IBMAEstimator's aggressive_mask from previous Datasets by @tsalo in https://github.com/neurostuff/NiMARE/pull/652 Use beta maps in PermutedOLS instead of z maps by @tsalo in https://github.com/neurostuff/NiMARE/pull/715 ### Other Changes Reduce SCALE memory usage by @tsalo in https://github.com/neurostuff/NiMARE/pull/632 Improve memory management in MKDAChi2 Estimator by @tsalo in https://github.com/neurostuff/NiMARE/pull/638 Remove Peaks2Maps-related tests by @tsalo in https://github.com/neurostuff/NiMARE/pull/643 Disable MA map pre-generation in CorrelationDecoder by @tsalo in https://github.com/neurostuff/NiMARE/pull/637 Switch testing from CircleCI to GitHub Actions by @tsalo in https://github.com/neurostuff/NiMARE/pull/642 Override unusable methods and improve documentation by @tsalo in https://github.com/neurostuff/NiMARE/pull/645 Document other meta-analysis tools outside our ecosystem by @tsalo in https://github.com/neurostuff/NiMARE/pull/654 Reorganize and streamline examples by @tsalo in https://github.com/neurostuff/NiMARE/pull/656 Convert CBMAEstimator method to function by @tsalo in https://github.com/neurostuff/NiMARE/pull/658 Add explicit support for Python 3.10 by @tsalo in https://github.com/neurostuff/NiMARE/pull/648 Use BibTeX citations in documentation by @tsalo in https://github.com/neurostuff/NiMARE/pull/670 Replace relative imports with absolute ones by @tsalo in https://github.com/neurostuff/NiMARE/pull/674 Simplify organization of base classes by @tsalo in https://github.com/neurostuff/NiMARE/pull/675 Note why we don't implement TFCE in NiMARE (currently) by @tsalo in https://github.com/neurostuff/NiMARE/pull/680 Dropping the memory-mapping option for Estimators and kernel transformers by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/681 Optimize locating coordinates in convert_neurosynth_to_dataset by @ryanhammonds in https://github.com/neurostuff/NiMARE/pull/682 Reduce memory usage of KernelTransformer.transform and meta.utils.compute_kda_ma by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/676 Generate automatic CHANGELOG from release note and add it to docs by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/684 Add manual changelog to documentation by @tsalo in https://github.com/neurostuff/NiMARE/pull/635 Automatically update CHANGELOG.md for prereleases as well by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/688 Fix tag-name issue in update-changelog workflow by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/689 Optimize numpy operations in MKDADensity Estimator and (M)KDAKernel by @adelavega in https://github.com/neurostuff/NiMARE/pull/685 Add PAT to automatically commit release notes to CHANGELOG.md by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/695 Fix CHANGELOG formatting issues by @JulioAPeraza in https://github.com/neurostuff/NiMARE/pull/701 Add citation information to documentation by @tsalo in https://github.com/neurostuff/NiMARE/pull/712 Add a glossary page to the documentation by @tsalo in https://github.com/neurostuff/NiMARE/pull/706 Remove extraneous copy() statements by @jdkent in https://github.com/neurostuff/NiMARE/pull/662 Add information about maintaining NiMARE to developer's guide by @tsalo in https://github.com/neurostuff/NiMARE/pull/703 Pin minimum version of pandas by @jdkent in https://github.com/neurostuff/NiMARE/pull/722 New Contributors @ryanhammonds made their first contribution in https://github.com/neurostuff/NiMARE/pull/682 @adelavega made their first contribution in https://github.com/neurostuff/NiMARE/pull/685 Full Changelog: https://github.com/neurostuff/NiMARE/compare/0.0.11...0.0.12
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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 | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,010 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,545 | 0,603 |
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 ».