{"id":"W2122899971","doi":"10.1109/prni.2013.23","title":"Mining the Hierarchy of Resting-State Brain Networks: Selection of Representative Clusters in a Multiscale Structure","year":2013,"lang":"en","type":"article","venue":"","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Hierarchy; Cluster analysis; Stability (learning theory); Hierarchical clustering; Selection (genetic algorithm); Computer science; Data mining; Reliability (semiconductor); Scale (ratio); Pattern recognition (psychology); Complex network; Task (project management); Resting state fMRI; Artificial intelligence; Machine learning; Physics; Cartography; Engineering; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.00118304,0.0006167285,0.0005824182,0.002603316,0.000593638,0.0009232039,0.0006260265,0.0005409899,0.0006883971],"category_scores_gemma":[0.005896091,0.0003498102,0.0006804337,0.001386708,0.0005298763,0.001024173,0.0007847882,0.0005354584,0.0002885608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004088429,"about_ca_system_score_gemma":0.0006417487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002686417,"about_ca_topic_score_gemma":0.004396361,"domain_scores_codex":[0.9995958,0.0001031201,0.00003079986,0.0001363441,0.00008711588,0.00004693668],"domain_scores_gemma":[0.9986334,0.0005771367,0.000261358,0.0001936861,0.0002564139,0.00007803441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008074024,0.0003837867,0.1184699,0.0008088567,0.0006059877,0.00104316,0.002801367,0.14903,0.1672526,0.02580814,0.009965318,0.5230235],"study_design_scores_gemma":[0.00004879194,0.0001465796,0.08981351,0.00008285056,0.0001428221,0.0004371431,0.0006328386,0.8379921,0.01233203,0.05587598,0.002426188,0.00006915192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3215505,0.0004884969,0.6747615,0.0002893831,0.00001427692,0.0001552599,0.0005811467,0.0006313937,0.001527988],"genre_scores_gemma":[0.8020889,0.0001828815,0.1961203,0.00004149293,0.00002264191,0.0001428528,0.0009467612,0.00008810322,0.0003661441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002686417,"threshold_uncertainty_score":0.00625658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02623219470334484,"score_gpt":0.2767801078606847,"score_spread":0.2505479131573399,"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."}}