{"id":"W1984505653","doi":"10.1002/acs.1011","title":"Blind identification of sparse Volterra systems","year":2007,"lang":"en","type":"article","venue":"International Journal of Adaptive Control and Signal Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Identifiability; Volterra series; Moment (physics); Independent and identically distributed random variables; Identification (biology); Mathematics; Applied mathematics; Volterra equations; System identification; Order (exchange); Computer science; Control theory (sociology); Algorithm; Nonlinear system; Random variable; Statistics; Artificial intelligence; Physics; Data modeling","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":[0.001776967,0.0000859138,0.0001833321,0.0003407907,0.00004211061,0.0002124945,0.0004733772,0.00004809664,0.000001756822],"category_scores_gemma":[0.00005239883,0.00007236951,0.00005950317,0.0001179442,0.00007353236,0.001042607,0.00003874564,0.000149775,7.450004e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004009661,"about_ca_system_score_gemma":0.0001118833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007559618,"about_ca_topic_score_gemma":0.000001326679,"domain_scores_codex":[0.9984213,0.00006241215,0.0007431514,0.0001206781,0.0005556084,0.00009689819],"domain_scores_gemma":[0.9970093,0.0001035321,0.001065383,0.0000612482,0.001698288,0.00006218851],"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":[0.001518849,0.0004407315,0.001665199,0.00005774566,0.0004890282,0.000171667,0.006072999,0.004206144,0.3196687,0.1490669,0.0002056694,0.5164365],"study_design_scores_gemma":[0.006940772,0.001065654,0.02430662,0.0009016737,0.00009158575,0.001051788,0.001325175,0.8808812,0.06117453,0.02024192,0.001491839,0.0005272062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02460722,0.0009253128,0.9734277,0.0004427611,0.0002027826,0.00007680929,0.000002280103,0.00001628855,0.0002988889],"genre_scores_gemma":[0.9936417,0.00001629425,0.005998185,0.0001316921,0.0001713121,0.000001111688,4.479896e-7,0.000004670984,0.00003462529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9690344,"threshold_uncertainty_score":0.2951143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02392144752691761,"score_gpt":0.2911995633237356,"score_spread":0.267278115796818,"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."}}