{"id":"W2007421864","doi":"10.1016/j.media.2009.09.005","title":"A nonlinear identification method to study effective connectivity in functional MRI","year":2009,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; McGill University","funders":"Canadian Institutes of Health Research; Institut National de la Santé et de la Recherche Médicale","keywords":"Identification (biology); Artificial intelligence; Computer science; Nonlinear system; Machine learning; Pattern recognition (psychology); Biology","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"],"consensus_categories":[],"category_scores_codex":[0.003070942,0.0002183926,0.000544886,0.0008673613,0.0002148979,0.00007781843,0.0002828382,0.00008136847,0.0005582373],"category_scores_gemma":[0.03649417,0.000198115,0.000240305,0.004771877,0.00008573322,0.0002885349,0.0001282795,0.0003793086,0.0002094887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001689449,"about_ca_system_score_gemma":0.00005374962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000162816,"about_ca_topic_score_gemma":0.0005062504,"domain_scores_codex":[0.9954217,0.001340663,0.0004477127,0.001113486,0.001347072,0.0003293778],"domain_scores_gemma":[0.9917716,0.007319534,0.0001065576,0.0004247319,0.0001532875,0.0002243219],"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":[0.001599607,0.02333515,0.1347389,0.000036932,0.002687346,0.001664526,0.008570548,0.006750311,0.4184607,0.0009900234,0.006622874,0.3945431],"study_design_scores_gemma":[0.001326917,0.0005909351,0.9017813,0.00000988433,0.0005933248,0.00001322962,0.0005870404,0.04451947,0.04866605,0.001163544,0.000379473,0.000368806],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5574415,0.00001204342,0.4228187,0.01835224,0.0001295892,0.0007238286,0.00001287419,0.0001023459,0.000406838],"genre_scores_gemma":[0.9913993,0.000002913855,0.002303471,0.005839063,0.0001490204,0.0001700715,0.000006563854,0.00001019787,0.0001193277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7670424,"threshold_uncertainty_score":0.9716219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02284182954468406,"score_gpt":0.3477557489552384,"score_spread":0.3249139194105544,"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."}}