{"id":"W2147242451","doi":"10.1109/icassp.2008.4518057","title":"Controlling the false discovery rate in modeling brain functional connectivity","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"False discovery rate; Computer science; Graphical model; Randomness; A priori and a posteriori; Conditional dependence; Functional connectivity; Artificial intelligence; Machine learning; Word error rate; Data mining; Pattern recognition (psychology); Neuroscience; Mathematics; Statistics; Psychology","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":[0.1744978,0.003127137,0.002650334,0.004535707,0.001300357,0.002522007,0.005682199,0.005849733,0.0009049877],"category_scores_gemma":[0.4102996,0.001397081,0.002784766,0.00342187,0.00694815,0.00385774,0.00323722,0.006205496,0.0004271051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002757022,"about_ca_system_score_gemma":0.003287284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005552433,"about_ca_topic_score_gemma":0.002752986,"domain_scores_codex":[0.8820332,0.09831969,0.003107358,0.007139601,0.008205531,0.001194611],"domain_scores_gemma":[0.4844061,0.4652104,0.01451006,0.02531675,0.009395937,0.001160844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002709977,0.0004235938,0.02569632,0.0009647967,0.002685071,0.001463811,0.001376709,0.6048206,0.01398893,0.1556329,0.00360576,0.1866316],"study_design_scores_gemma":[0.0002259855,0.0003536271,0.002555939,0.0001171934,0.0002902666,0.0004420881,0.00004122194,0.9131214,0.00870994,0.07223818,0.001763169,0.0001409924],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004648904,0.0004017879,0.993519,0.0003397668,0.00008876067,0.0001177411,0.0000677229,0.0005938654,0.0002223203],"genre_scores_gemma":[0.3348101,0.0007034763,0.6597514,0.0008213455,0.0003626767,0.001397306,0.0003086074,0.0005862361,0.001258834],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1744978,"threshold_uncertainty_score":0.9228438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08739224851049968,"score_gpt":0.2826146315219698,"score_spread":0.1952223830114701,"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."}}