{"id":"W2041766181","doi":"10.1016/j.asoc.2009.02.006","title":"Modified fuzzy c-means and Bayesian equalizer for nonlinear blind channel","year":2009,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Bayesian probability; Equalizer; Nonlinear system; Computer science; Fuzzy logic; Channel (broadcasting); Blind equalization; Control theory (sociology); Artificial intelligence; Telecommunications; Physics","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":[0.001179385,0.0005180039,0.000751108,0.0006409229,0.0006124192,0.0008391212,0.001019476,0.00143366,0.001264844],"category_scores_gemma":[0.004068694,0.0003354381,0.0005991965,0.0009312099,0.0007106237,0.001179541,0.0005750459,0.00120133,0.0003280794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008583157,"about_ca_system_score_gemma":0.001577309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01196936,"about_ca_topic_score_gemma":0.01307473,"domain_scores_codex":[0.9992582,0.0001812697,0.00004914598,0.0001401749,0.0003107111,0.00006045289],"domain_scores_gemma":[0.9988814,0.000479321,0.0000598587,0.00007546885,0.0004797438,0.0000241328],"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":[0.0005334072,0.0001029049,0.0004490269,0.0001855971,0.0001529567,0.00007450335,0.0001494973,0.6240348,0.01947313,0.04424183,0.002587955,0.3080144],"study_design_scores_gemma":[0.000008997184,0.00001422508,0.0001407073,0.000004576912,0.00001204465,0.00001794494,0.000005051561,0.9923666,0.002220233,0.004805995,0.0003881089,0.00001544083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005673445,0.0002319564,0.993523,0.00006226676,0.00003671865,0.000009222096,0.00001349479,0.00006118874,0.0003885928],"genre_scores_gemma":[0.2549163,0.0005841586,0.7394645,0.0001200299,0.0001064386,0.0001057561,0.00008440971,0.00004770518,0.004570726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01196936,"threshold_uncertainty_score":0.02379936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02683636772028091,"score_gpt":0.2882282620789368,"score_spread":0.2613918943586558,"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."}}