{"id":"W1552537573","doi":"10.1007/978-3-540-72432-2_10","title":"MFCM for Nonlinear Blind Channel Equalization","year":2007,"lang":"en","type":"book-chapter","venue":"Advances in soft computing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Algorithm; Nonlinear system; Channel (broadcasting); Simulated annealing; Blind equalization; Bayesian probability; Mathematics; Gaussian; Computer science; Mathematical optimization; Equalization (audio); Statistics; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001076179,0.0003336239,0.0004091129,0.0003652923,0.0001534941,0.0001265613,0.0008913094,0.0003380121,0.000007792693],"category_scores_gemma":[0.00009853771,0.0003890748,0.0001274402,0.0001359783,0.00006848892,0.0005936162,0.0003277023,0.0003929614,0.00002686484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008675834,"about_ca_system_score_gemma":0.00008619072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002728134,"about_ca_topic_score_gemma":0.00004696703,"domain_scores_codex":[0.9979005,0.00002454869,0.0006430775,0.00070699,0.0003543548,0.000370509],"domain_scores_gemma":[0.9980917,0.0005451525,0.000540238,0.0005163296,0.0002415292,0.00006506123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001934806,0.00003121611,0.000005936431,0.0001147788,0.0000127922,0.00001033717,0.001555153,0.009334042,0.000005258139,0.5153275,0.0002271683,0.4733565],"study_design_scores_gemma":[0.0004896082,0.0001021523,0.000001552359,0.0004107984,0.000006087701,0.000008047837,0.00001272061,0.3976768,0.0001280341,0.1720088,0.4286493,0.0005061543],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000003311506,0.002522565,0.9042333,0.0001070879,0.0005127459,0.0005933509,0.000006292614,0.0004875179,0.09153382],"genre_scores_gemma":[0.001571113,0.0005910373,0.9803546,0.001475177,0.0007294352,0.0000127579,0.0001372828,0.00009878433,0.01502978],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4728504,"threshold_uncertainty_score":0.9998561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04967422445286042,"score_gpt":0.3514324561832285,"score_spread":0.3017582317303681,"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."}}