{"id":"W2529237564","doi":"10.1007/s11336-016-9521-1","title":"Functional Generalized Structured Component Analysis","year":2016,"lang":"en","type":"article","venue":"Psychometrika","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute on Drug Abuse","keywords":"Functional data analysis; Component (thermodynamics); Mathematics; Component analysis; Function (biology); Multivariate statistics; Structural equation modeling; Computer science; Applied mathematics; Econometrics; Algorithm; Statistics; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003425866,0.0001226525,0.0001971524,0.001263472,0.00007081772,0.0001113123,0.0005444137,0.00006418052,0.0004444835],"category_scores_gemma":[0.00006748401,0.00008112801,0.0001798384,0.003493495,0.00003210861,0.0003248968,0.0000804753,0.00006248114,0.0001154099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004953071,"about_ca_system_score_gemma":0.00002335889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001013599,"about_ca_topic_score_gemma":0.00000846659,"domain_scores_codex":[0.9986432,0.0001065478,0.0002428204,0.0003981003,0.0004170799,0.0001922411],"domain_scores_gemma":[0.9988403,0.0001723579,0.0001078319,0.0006650347,0.0001124379,0.0001020641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00008327237,0.0003335237,0.04126111,0.000007191523,0.001545348,0.000006311493,0.0004804374,0.0001933764,0.06379541,0.5693951,0.06809408,0.2548048],"study_design_scores_gemma":[0.00249093,0.0001434492,0.7970164,0.00001063971,0.0001313824,0.00001340728,0.000006793647,0.003787157,0.03586858,0.0358263,0.1239825,0.0007224755],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04771008,0.00009682208,0.9468973,0.003322188,0.0003158029,0.00009480825,0.000006509001,0.0004256617,0.001130868],"genre_scores_gemma":[0.9224433,0.00003574887,0.07562117,0.0008310917,0.00006597029,0.00001992024,0.000007713141,0.000006988144,0.0009680939],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8747332,"threshold_uncertainty_score":0.4866783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03016892148332491,"score_gpt":0.2799234535781663,"score_spread":0.2497545320948414,"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."}}