Notice bibliographique
Résumé
Release Notes This release includes a number of substantial changes to NiMARE. Major changes We've added PyMARE as a dependency! PyMARE is a general-purpose meta-analysis library in Python that we now use to perform our image-based meta-analyses. For image-based meta-analyses, we also now have a transforms module to calculate new image types from available data. Datasets now have a number of attributes retained as properties, which will break compatibility with Datasets from older versions of NiMARE. We now have multiple methods for converting summary statistics (e.g., ALE, OF) to p-values in all of our major CBMA algorithms, thanks to @tyarkoni! The two current methods for each algorithm are a fast, but slightly less accurate, "analytic" method and a slower, but more accurate, "empirical" method. For ALE, We generally recommend the "analytic" method for maximum compatibility with GingerALE. The implementations of these algorithms have also been streamlined and sped up somewhat. We have a new generate module for simulating coordinate-based datasets, thanks to @jdkent! A number of modules, classes, and functions that were not yet implemented have been pruned from the API to make it easier to work with. Don't worry, we're still planning to get around to them at some point. Changes [FIX] Fix the warnings about mismatched kernels and estimators (#425) @tsalo [FIX] Add nullhist_to_p and crop invalid p-values (#409) @tsalo [TST] Do not download test peaks2maps to tmpdir (#419) @tsalo [FIX] Restructure Peaks2MapsKernel to operate like other kernels (#410) @tsalo [ENH] Improve convergence between ALE null methods (#411) @tsalo [DOC] Add warnings for CBMA kernel/estimator mismatch (#416) @tsalo [FIX] Remove rows with empty abstract before running LDAModel (#414) @JulioAPeraza [FIX] Sort all arrays and DataFrames in Dataset by ID (#402) @tsalo [FIX] Allow no coordinates in a dataset (#407) @jdkent [ENH] Add analytic null method to KDA estimator (#397) @tsalo [FIX] Use unzipped mask as temporary fix (#401) @tsalo [DOC] Update API and examples (#395) @tsalo [REF] CBMA re-organization and improvement (#393) @tyarkoni [MAINT] Pin to PyMARE 0.0.2 (#391) @tsalo [TST] Test both analytic and empirical methods in ALE and MKDA (#380) @jdkent [FIX] Change default seed to None (#392) @jdkent [PERF] Various performance improvements (#386) @tyarkoni Add performance tweaks to ALE analytical null generation (#390) @tyarkoni fix tests (#387) @tyarkoni [FIX] respect n_noise_foci value (#382) @jdkent [ENH] Add analytic null method to MKDADensity (#375) @tsalo [ENH] Add empirical null method to density-based CBMA Estimators (#372) @tsalo [REF] Refactor KernelTransformer hierarchy (#369) @tyarkoni [ENH] Add generate module (#343) @jdkent [FIX] enforce correct lowest p-value (#365) @jdkent [FIX] Treat vfwe as an array of floats for KDA (#362) @jdkent [DOC] Update roadmap.rst (#359) @tsalo [DOC] Add example of combining kernels and CBMA estimators (#346) @koudyk [MAINT] Add Dorota Jarecka to Zenodo file (#358) @djarecka [MAINT] Add Enrico Glerean's affiliation and ORCID (#357) @eglerean [ENH] Clip p-values based on number of permutations (#353) @tsalo [REF] Remove unused alpha argument in statsmodels call (#354) @tsalo [ENH] Replace TTest with PermutedOLS (#304) @tsalo [REF] Reduce dependencies (#345) @tsalo [ENH] Add Neurosynth data fetcher (#342) @tsalo [INFRA] Add json describing filename convention (#338) @tsalo [DOC] Enable CBMA example (#337) @tsalo [FIX] Add private setter method for Dataset.ids (#336) @tsalo [REF] More low-memory work (#334) @tsalo [FIX, DOC] Change natural log to base-ten and document output naming convention (#333) @tsalo [FIX] Pin setuptools again (#331) @tsalo [FIX] Update setuptools version (#330) @tsalo [FIX] Add setuptools to requirements (#329) @tsalo [TST] Add test for peaks2maps (#328) @tsalo [FIX, TST] Fix and test CorrelationDistributionDecoder (#327) @tsalo [TST] Use temporary directories with automatic teardown (#326) @tsalo [REF] Speed up CorrelationDecoder (#324) @tsalo [ENH] Support Dataset transformations in kernel transformers (#320) @tsalo [ENH] Add PairwiseCBMAEstimator class and add low_memory option to ALESubtraction (#319) @tsalo [TST] Improve meta-analysis tests (#318) @tsalo [DOC] Fix Lancaster xform and Sleuth conversion docstrings (#317) @tsalo [TST] Improve nimare.io test coverage (#314) @tsalo [REF] Reduce duplication by calling _check_ncores (#313) @tsalo [REF] Remove generate_cooccurrence (#312) @tsalo [REF] Operate on arrays in ALESubtraction (#311) @tsalo [TST] Add flake8-black to test requirements (#300) @akimbler [FIX] Support multiple header lines in Sleuth text files (#310) @tsalo [FIX] Operate on copy of df in extract_cogat() (#306) @tsalo [MAINT] Update setup configuration (#303) @tsalo [REF] Sort imports alphabetically (#299) @tsalo [REF] Run automated code formatting with black (#296) @tsalo [DOC] Remove whitespace from README (#295) @tsalo [MAINT, TST] Drop 3.5 support. Add tests for Python 3.7 and 3.8. (#293) @tsalo [MAINT] Delete unused files (#291) @tsalo [MAINT] Increase minimum tensorflow to 2.0.0 (#290) @tsalo [FIX] Update peaks2maps w.r.t. recent changes in the API (#287) @tsalo [FIX] Raise an error in Decoders if no features remain (#284) @tsalo [REF] Move CBMA methods up a level (#283) @tsalo [REF] Rename RandomEffectsGLM to TTest (#282) @tsalo [ENH] Split DerSimonianLaird and Hedges IBMA estimators (#281) @tsalo [DOC] Expand IBMA example (#280) @tsalo [ENH] Use PyMARE for image-based meta-analyses (#273) @tsalo [FIX] Replace NaNs in Datasets with Nones (#276) @tsalo [ENH] Support initialized and uninitialized kernels for CBMA (#275) @tsalo [ENH] Add functions to convert image types (#272) @tsalo [REF] Convert Dataset attributes to properties (#270) @tsalo [REF] Drop unimplemented annotators (#269) @tsalo [REF] Drop unimplemented parcellate module and meta-ICA workflow (#264) @tsalo [ENH] Use nearest-neighbor interpolation for masks (#258) @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 | 0,003 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,006 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,427 | 0,494 |
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 ».