{"id":"W4248334202","doi":"10.12688/mniopenres.12767.1","title":"MIST: A multi-resolution parcellation of functional brain networks","year":2017,"lang":"en","type":"article","venue":"MNI Open Research","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institut Universitaire en Santé Mentale de Québec; McGill University; Institut Universitaire de Gériatrie de Montréal; Montreal Neurological Institute and Hospital","funders":"Fonds de Recherche du Québec - Santé; Azrieli Foundation; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Connectomics; Functional connectivity; Computer science; Cartography; Spatial normalization; Artificial intelligence; Geography; Neuroscience; Connectome; Biology; Voxel","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.00284977,0.0001053236,0.0001768386,0.0001309247,0.002111299,0.0003925027,0.0009873867,0.00007833706,0.000259147],"category_scores_gemma":[0.02776018,0.00009972588,0.00005632267,0.0002240807,0.0006789384,0.0005033219,0.00138206,0.0003802498,0.0001327837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001055476,"about_ca_system_score_gemma":0.0001235158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007202483,"about_ca_topic_score_gemma":0.000410595,"domain_scores_codex":[0.9974486,0.000556165,0.0002085997,0.000562649,0.0008216937,0.0004022702],"domain_scores_gemma":[0.9934561,0.005273345,0.0001405952,0.000731791,0.0003186946,0.00007947371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002595051,0.001351434,0.05615402,0.0001308961,0.00008982522,0.00006169979,0.0006975165,0.0278074,0.4216273,0.05976674,0.4085047,0.02121342],"study_design_scores_gemma":[0.003080535,0.0004478625,0.6081073,0.0001111545,0.000007714388,0.00001752633,0.0001799327,0.2923604,0.03787271,0.003649653,0.05379074,0.0003744489],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5529743,0.0003824345,0.05523152,0.1360152,0.002993557,0.006444161,0.0001330547,0.0001801167,0.2456457],"genre_scores_gemma":[0.9872023,0.00001828632,0.000320031,0.0003427979,0.0001757603,0.00009299459,0.000004850518,0.00001584111,0.01182717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5519533,"threshold_uncertainty_score":0.9991878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4633745538975311,"score_gpt":0.4637603814765836,"score_spread":0.0003858275790525334,"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."}}