{"id":"W2127238581","doi":"10.1109/vetec.1995.504900","title":"A PHASE algorithm for blind adaptive optimum diversity combining","year":2002,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Algorithm; Cyclostationary process; Computer science; Diversity combining; Interference (communication); Wireless; Sample matrix inversion; Computation; Adaptive filter; Rate of convergence; Computational complexity theory; Adaptive algorithm; Channel (broadcasting); Covariance matrix; Telecommunications; Fading; Decoding methods","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":[0.0002241473,0.00009048926,0.0001088151,0.00009325481,0.0002634616,0.0001021762,0.0005210998,0.00005267133,0.00006236307],"category_scores_gemma":[0.00001208611,0.00008756564,0.00006335547,0.000199034,0.00002827179,0.0005604433,0.0004253874,0.00008048554,0.0000382223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002432202,"about_ca_system_score_gemma":0.000009860541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001381301,"about_ca_topic_score_gemma":0.000001895113,"domain_scores_codex":[0.9992539,0.00003350651,0.0001187983,0.0002579064,0.0001623448,0.000173499],"domain_scores_gemma":[0.9994156,0.0000928485,0.00005560204,0.000258347,0.0001054794,0.00007205885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000191574,0.001168836,0.00001161657,0.000003170476,0.00004500876,0.000009991209,0.006717819,0.00004106212,0.00009236552,0.1792626,0.03442566,0.7782027],"study_design_scores_gemma":[0.002367246,0.000612018,0.000002998039,0.00000357866,0.000004262372,0.000003617952,0.00006152358,0.9866104,0.002764506,0.004278555,0.003152498,0.0001388555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007552592,0.00002038432,0.9881091,0.0006204911,0.00007253997,0.0003010441,0.000004674901,0.00050029,0.009616247],"genre_scores_gemma":[0.2010402,0.000004573477,0.796517,0.0008543647,0.00002214202,0.00002124337,0.000001820513,0.00000469302,0.001533935],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9865693,"threshold_uncertainty_score":0.3570823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07690110843069478,"score_gpt":0.3032376360139325,"score_spread":0.2263365275832377,"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."}}