{"id":"W2145539542","doi":"10.1109/tmi.2011.2116034","title":"A Model Selection Method for Nonlinear System Identification Based fMRI Effective Connectivity Analysis","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Akaike information criterion; Nonlinear system; Computer science; Model selection; Selection (genetic algorithm); Artificial intelligence; Cross-validation; Residual; Functional magnetic resonance imaging; Block (permutation group theory); System identification; Identification (biology); Algorithm; Mathematics; Pattern recognition (psychology); Machine learning; Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001591049,0.0002690934,0.0004184682,0.0006543275,0.0007522664,0.00005139101,0.0002262418,0.0001161045,0.00007054583],"category_scores_gemma":[0.002025131,0.0002629202,0.0004274491,0.001438831,0.000164277,0.0003444316,0.000002950113,0.0004347768,0.00002819201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003465923,"about_ca_system_score_gemma":0.0001430261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001502292,"about_ca_topic_score_gemma":0.000175318,"domain_scores_codex":[0.9969153,0.0005639614,0.0004058785,0.0009880227,0.0007499125,0.0003769486],"domain_scores_gemma":[0.9919323,0.007155025,0.000167798,0.0003176362,0.0002334766,0.0001937477],"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":[0.002665087,0.003550977,0.0009084056,0.0007757357,0.001857167,0.00005195176,0.002618757,0.4521968,0.2973572,0.003128334,0.0008758597,0.2340137],"study_design_scores_gemma":[0.0005883409,0.00005529197,0.0001947253,0.00002922389,0.0004774206,0.00001346348,0.0000924722,0.7225692,0.2756245,0.0001492428,0.00002611532,0.00018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008534062,0.000006258533,0.9879095,0.001178038,0.0006623759,0.0008773092,0.0001075553,0.0004537606,0.0002711668],"genre_scores_gemma":[0.9762288,0.000002365885,0.02127738,0.001381724,0.00007364811,0.0009190361,0.00000362931,0.00003741725,0.00007596464],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9676948,"threshold_uncertainty_score":0.9999823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04360683359865434,"score_gpt":0.3181672025048474,"score_spread":0.274560368906193,"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."}}