{"id":"W4386332849","doi":"10.52294/001c.87681","title":"NiMARE: Neuroimaging Meta-Analysis Research Environment","year":2023,"lang":"en","type":"article","venue":"Aperture Neuro","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Neuroimaging; Meta-analysis; Psychology; Medicine; Neuroscience; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02207794,0.003650113,0.003965194,0.00656443,0.001243754,0.006075705,0.007444298,0.002304118,0.1445979],"category_scores_gemma":[0.1039271,0.003791274,0.009511806,0.006502785,0.001050242,0.003891742,0.007661026,0.003917532,0.05469923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002285333,"about_ca_system_score_gemma":0.01113389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008695086,"about_ca_topic_score_gemma":0.0176886,"domain_scores_codex":[0.9910852,0.004506578,0.001193269,0.001438354,0.001458713,0.0003178054],"domain_scores_gemma":[0.9584009,0.03074927,0.003013475,0.004961351,0.001973449,0.0009016127],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00174593,0.0001825357,0.005318397,0.0182647,0.009611201,0.0007693226,0.0007965485,0.01047558,0.001412684,0.03131766,0.8020816,0.1180237],"study_design_scores_gemma":[0.005049824,0.0002328047,0.006950957,0.002832857,0.004706932,0.001002352,0.0001093803,0.03480182,0.004002248,0.1505151,0.7891903,0.0006053551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.001826375,0.003057521,0.3369374,0.002720715,0.0006964183,0.00212948,0.3372537,0.304954,0.01042436],"genre_scores_gemma":[0.02582343,0.002927344,0.7035118,0.004265606,0.0005626464,0.02590502,0.1244925,0.1013621,0.01114973],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9779221,"threshold_uncertainty_score":0.4837281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3171947735619751,"score_gpt":0.3703831420235524,"score_spread":0.05318836846157737,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}