{"id":"W1491285536","doi":"10.1007/978-3-540-27824-5_77","title":"Detection of Equalization Errors in Time-Varying Channels","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Robustness (evolution); Equalization (audio); Channel (broadcasting); Algorithm; Computational complexity theory; Adaptive equalizer; Blind equalization; 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":[0.001244191,0.0003266962,0.0004179429,0.001633494,0.00008713701,0.0002308229,0.001888312,0.0003457335,0.00001208065],"category_scores_gemma":[0.00008874899,0.0003378257,0.00008408975,0.001062861,0.0002789253,0.0007764198,0.0006288578,0.0005219589,0.00001617854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003860495,"about_ca_system_score_gemma":0.0004028848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005418379,"about_ca_topic_score_gemma":0.00005090702,"domain_scores_codex":[0.9971807,0.00005897121,0.0006222993,0.0009183638,0.0008672391,0.0003524259],"domain_scores_gemma":[0.9983741,0.0001863788,0.0004029618,0.0007702654,0.0001979197,0.00006834882],"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":[0.00001325925,0.00008611551,0.00004416341,0.00009629373,0.000009076578,0.00003452269,0.005832274,0.5170961,0.01165672,0.0529669,0.000002229176,0.4121623],"study_design_scores_gemma":[0.0003022634,0.0002099979,0.00005618913,0.0007112551,0.000003225611,0.00002009204,1.215148e-7,0.5493593,0.1160016,0.3327501,0.00008190071,0.0005039119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005450252,0.0001003943,0.9968373,0.0002228804,0.0005120998,0.0004025158,0.000001138663,0.0001987704,0.001179863],"genre_scores_gemma":[0.8251587,0.00002171626,0.1740433,0.0005446168,0.00009836307,0.0000102017,0.000004283602,0.00002625326,0.00009258633],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8246137,"threshold_uncertainty_score":0.9999074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0205119131974827,"score_gpt":0.2658428438431196,"score_spread":0.2453309306456369,"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."}}