{"id":"W3207235303","doi":"10.82308/33579","title":"Dynamic GSCA generalized structured component analysis: a structural equation model for analyzing effective connectivity in functional neuroimaging","year":2012,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University","keywords":"Structural equation modeling; Component (thermodynamics); Neuroimaging; Component analysis; Computer science; Independent component analysis; Psychology; Cognitive psychology; Econometrics; Artificial intelligence; Neuroscience; Economics; Machine learning; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004645559,0.001437447,0.001250037,0.003251725,0.0007535801,0.001394348,0.002077038,0.001106546,0.003831484],"category_scores_gemma":[0.01575447,0.0005757078,0.002124669,0.003865744,0.001375768,0.002140365,0.001764436,0.00208423,0.0005556226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001536854,"about_ca_system_score_gemma":0.004032667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01305438,"about_ca_topic_score_gemma":0.02216895,"domain_scores_codex":[0.9966846,0.00222412,0.0001117465,0.0005342507,0.0003575096,0.00008771149],"domain_scores_gemma":[0.9960983,0.00263981,0.0003536398,0.0003890339,0.0004263156,0.00009293425],"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":[0.0001486092,0.0002533004,0.01542506,0.0006914818,0.001798502,0.0003937509,0.001608183,0.2031521,0.004949864,0.3671111,0.02231338,0.3821546],"study_design_scores_gemma":[0.00005443218,0.000159952,0.008470251,0.0001488629,0.0002604802,0.0002490433,0.0002155267,0.7055438,0.0009788681,0.27244,0.01135013,0.0001286934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009950113,0.000411487,0.9867933,0.0005530764,0.00006594098,0.0002084806,0.0004107484,0.000393629,0.001213114],"genre_scores_gemma":[0.1705545,0.001033657,0.8232669,0.0002588534,0.0001014675,0.001450494,0.001228513,0.0002228118,0.001882802],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01305438,"threshold_uncertainty_score":0.02595675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04409464436491919,"score_gpt":0.275671842126353,"score_spread":0.2315771977614338,"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."}}