{"id":"W2626537255","doi":"10.1101/149567","title":"Beyond Consensus: Embracing Heterogeneity in Curated Neuroimaging Meta-Analysis","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute of Mental Health; Fonds de Recherche du Québec - Santé; European Commission; Ministry of Education, India; National Medical Research Council; National Research Foundation Singapore; Canadian Institutes of Health Research; National Research Foundation","keywords":"Set (abstract data type); Neuroimaging; Domain (mathematical analysis); Independent component analysis; Cognition; Task (project management); Functional neuroimaging","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"category_scores_codex":[0.3456182,0.004191855,0.01120216,0.01234599,0.001669243,0.01087253,0.008654236,0.00554687,0.003420995],"category_scores_gemma":[0.5985958,0.00285556,0.02549805,0.01078745,0.004689456,0.008421239,0.006926748,0.007748692,0.0005311078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003109932,"about_ca_system_score_gemma":0.004384278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003317931,"about_ca_topic_score_gemma":0.004374288,"domain_scores_codex":[0.5496809,0.3788419,0.02333888,0.03527161,0.01159497,0.001271737],"domain_scores_gemma":[0.2463505,0.6896668,0.01330963,0.04574769,0.003994361,0.0009309427],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002943791,0.0001850127,0.04173094,0.04769759,0.5799695,0.002748231,0.002499736,0.1112246,0.003655209,0.07002421,0.01366355,0.1236576],"study_design_scores_gemma":[0.001527643,0.0006333356,0.01247448,0.006724238,0.1377052,0.000657838,0.0005821178,0.2426248,0.00303417,0.5776938,0.01579588,0.0005465336],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01659962,0.03014954,0.9391832,0.005079183,0.001073162,0.001508383,0.002192529,0.002677003,0.001537402],"genre_scores_gemma":[0.6018384,0.004654786,0.3795263,0.004393311,0.0008572167,0.005347105,0.0017903,0.001138218,0.0004544924],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6543818,"threshold_uncertainty_score":0.8069692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08427219930635985,"score_gpt":0.2912336894161783,"score_spread":0.2069614901098185,"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."}}