{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005487002,0.0004125096,0.0007223588,0.0003995135,0.0002140816,0.0005059108,0.0003739291,0.0006015185,0.0009593428],"category_scores_gemma":[0.003413806,0.0002119444,0.0003563987,0.0002741165,0.0005486358,0.000707034,0.0007097986,0.0006173245,0.0002192628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002129958,"about_ca_system_score_gemma":0.0004129012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007082752,"about_ca_topic_score_gemma":0.0004724614,"domain_scores_codex":[0.9997024,0.00007757125,0.00001899365,0.00007357871,0.0001003907,0.00002709873],"domain_scores_gemma":[0.9991648,0.0004364779,0.0001604248,0.00009880346,0.0001182572,0.00002116572],"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":[0.0003903596,0.00005779986,0.001177274,0.0002706408,0.00008918583,0.0002537173,0.0001799258,0.7086362,0.07114356,0.05353436,0.001321272,0.1629457],"study_design_scores_gemma":[0.000007046597,0.00001686029,0.000184066,0.000005561441,0.000005243947,0.00005963446,0.000009067609,0.9846222,0.004551602,0.01019275,0.0003384456,0.000007540893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04926261,0.0001442842,0.9494345,0.00008772998,0.00003706179,0.00001670952,0.00003180868,0.0001760752,0.0008093031],"genre_scores_gemma":[0.897893,0.0002330812,0.09913219,0.00005952927,0.00005392268,0.00004660473,0.0001062522,0.00002795791,0.002447468],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0009593428,"threshold_uncertainty_score":0.003209293,"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."}}