{"id":"W1960297826","doi":"10.1109/icc.1988.13591","title":"An investigation of block-adaptive decision feedback equalization for frequency selective fading channels","year":2003,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Bell (Canada)","funders":"","keywords":"Fading; Block (permutation group theory); Equalization (audio); Computer science; Adaptation (eye); Channel (broadcasting); Interpolation (computer graphics); Algorithm; Mathematics; Artificial intelligence; Telecommunications; Psychology; Combinatorics","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":[0.0004140847,0.0002484434,0.0002954271,0.0001509652,0.0002161262,0.0002943798,0.0003370008,0.00048854,0.002596071],"category_scores_gemma":[0.001880853,0.0001342977,0.0001897044,0.0002360764,0.0002811685,0.00060529,0.0002010659,0.000327018,0.000256729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004157428,"about_ca_system_score_gemma":0.0003665319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00281128,"about_ca_topic_score_gemma":0.003047105,"domain_scores_codex":[0.9998661,0.00005026548,0.00000314977,0.00000810397,0.0000550906,0.00001730436],"domain_scores_gemma":[0.9991598,0.0006532997,0.00003810094,0.00003104979,0.0001072938,0.00001047238],"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":[0.000099828,0.00003897483,0.0005420455,0.0001079495,0.00002671616,0.0000940774,0.00005958377,0.9308439,0.01180096,0.02892279,0.0005837754,0.0268795],"study_design_scores_gemma":[0.00000395389,0.00003077327,0.0001044732,0.000004447564,0.000002889799,0.00002118803,0.00000496101,0.9973242,0.0008649116,0.001359387,0.0002764288,0.000002300155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.123163,0.001250738,0.8615763,0.0003150823,0.00009606279,0.00008588115,0.00006012494,0.0004161992,0.01303662],"genre_scores_gemma":[0.9262809,0.001086706,0.06731179,0.00006671406,0.00002445563,0.00006522863,0.00003813852,0.00004180819,0.005084373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00281128,"threshold_uncertainty_score":0.008684754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03708153234621106,"score_gpt":0.3005362899304265,"score_spread":0.2634547575842154,"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."}}