{"id":"W2120681317","doi":"10.1109/lcomm.2003.812177","title":"Comparison of MOE and blind LMS","year":2003,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Convergence (economics); Code division multiple access; Algorithm; Least mean squares filter; Adaptive filter; Steady state (chemistry); Computer science; Interference (communication); Mean squared error; Stability (learning theory); Sequence (biology); Mathematics; Telecommunications; Statistics; Channel (broadcasting)","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":[0.00006457501,0.00007485486,0.000121834,0.00006684948,0.00004701839,0.000007641059,0.0002822127,0.00002737067,0.000004090208],"category_scores_gemma":[0.00001746044,0.00008560409,0.00001860485,0.000101647,0.0001333509,0.00007554964,0.00003532069,0.0001420657,0.000003376127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002370284,"about_ca_system_score_gemma":0.0000025097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003133249,"about_ca_topic_score_gemma":0.000004785734,"domain_scores_codex":[0.9996006,0.00003542273,0.0001666197,0.00006122742,0.00004793642,0.00008817484],"domain_scores_gemma":[0.9990385,0.00008679085,0.00003299088,0.0007993753,0.00001823364,0.00002412659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000004100827,0.0000693024,0.004847612,0.0000531742,0.00005442376,4.63221e-7,0.0008968782,0.01191603,0.9647293,0.008552063,0.004685754,0.004190902],"study_design_scores_gemma":[0.0008774048,0.00007074224,0.004873565,0.0001826065,0.00004985563,0.00001554722,0.0002994385,0.03580203,0.8177444,0.001935592,0.1374126,0.0007361498],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6608598,0.001505358,0.331019,0.0006139204,0.0001160545,0.0002458683,0.00001185281,0.0005334464,0.005094641],"genre_scores_gemma":[0.8749435,0.0001374828,0.1248127,0.00005647914,0.000003927442,0.00002078887,0.00000298945,0.00001547191,0.000006639173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2140837,"threshold_uncertainty_score":0.3490833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0692333979795927,"score_gpt":0.3358493280256099,"score_spread":0.2666159300460171,"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."}}