{"id":"W2964002974","doi":"10.1111/rssc.12169","title":"Estimating Whole-Brain Dynamics by Using Spectral Clustering","year":2016,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health","funders":"Engineering and Physical Sciences Research Council; Alberta Health Services","keywords":"Computer science; Data mining; Cluster analysis; Series (stratigraphy); Node (physics); Spectral clustering; Data set; Artificial intelligence; Set (abstract data type); Time series; Multivariate statistics; Pattern recognition (psychology); Algorithm; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.001294967,0.0006004588,0.0005419127,0.002437339,0.0003596744,0.0008149896,0.0008173367,0.0006265888,0.001233091],"category_scores_gemma":[0.006003573,0.000355043,0.0006813217,0.001534537,0.0005699046,0.001307105,0.0006743621,0.0007434294,0.0004677115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005234336,"about_ca_system_score_gemma":0.000529425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00440575,"about_ca_topic_score_gemma":0.004315878,"domain_scores_codex":[0.9995389,0.0001524881,0.00002361832,0.000166783,0.00009201185,0.00002622845],"domain_scores_gemma":[0.9982761,0.0009698093,0.000224411,0.0002714045,0.0002008159,0.00005743577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001212298,0.00007159943,0.007241332,0.0001346477,0.0003445164,0.0001515383,0.0001895307,0.7959429,0.01721869,0.0219829,0.002188274,0.1544128],"study_design_scores_gemma":[0.000002149694,0.000009133651,0.00203683,0.000007103885,0.000009056896,0.00002640222,0.00002072138,0.9759442,0.0008662355,0.0205652,0.0005004955,0.00001255078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05484559,0.0003591358,0.9431034,0.0001658494,0.00002033858,0.00003445107,0.000202654,0.0004358535,0.0008325775],"genre_scores_gemma":[0.7498453,0.0004527416,0.2468543,0.00005322779,0.00007144204,0.00008935085,0.0009979551,0.0002156651,0.001420031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00440575,"threshold_uncertainty_score":0.008760214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01806090013214348,"score_gpt":0.2576678354307227,"score_spread":0.2396069352985792,"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."}}