{"id":"W1531959055","doi":"10.1109/pacrim.1997.619994","title":"Generalized DLMS algorithm","year":2002,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Convergence (economics); Residual; Algorithm; Architecture; Least mean squares filter; Algorithm design; Adaptive filter","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":[0.0001078459,0.000054918,0.0000592856,0.00005356806,0.00003921202,0.0001105173,0.0003897594,0.0000319913,0.0004111246],"category_scores_gemma":[0.000005401214,0.00004630597,0.0000306832,0.0001844738,0.00001135622,0.0003089627,0.00007980708,0.0000487434,0.0003761515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008698356,"about_ca_system_score_gemma":0.000004125843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001323458,"about_ca_topic_score_gemma":0.00000134153,"domain_scores_codex":[0.9994714,0.00003625956,0.00009597959,0.0001564133,0.0001313726,0.000108596],"domain_scores_gemma":[0.9995673,0.00001419247,0.00002165054,0.0003233092,0.00003084336,0.0000427424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[1.118599e-7,0.00004449515,0.00001243692,6.113019e-7,0.000004288955,0.000004474066,0.0005158262,0.000006813512,0.0003430622,0.5520998,0.08068503,0.3662831],"study_design_scores_gemma":[0.0002000541,0.0000397419,0.00006816904,0.000001567902,8.890856e-7,0.00001456555,0.000004161844,0.8059443,0.01831476,0.01133225,0.1639204,0.0001591691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003306337,0.00004843607,0.9337265,0.002420323,0.000062887,0.00005581491,1.942374e-7,0.0008340361,0.06252119],"genre_scores_gemma":[0.022801,0.00002637725,0.9600295,0.003852417,0.00003152636,0.0000111971,5.163386e-7,0.00000426963,0.01324313],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8059375,"threshold_uncertainty_score":0.4834792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02813864554737143,"score_gpt":0.2535802419952585,"score_spread":0.225441596447887,"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."}}