{"id":"W1554627082","doi":"10.1109/pacrim.2005.1517295","title":"Estimation of fading distribution parameters in noisy channels","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Fading; Nakagami distribution; Channel (broadcasting); Noise (video); Statistics; Computer science; Moment (physics); Algorithm; Mathematics; Noise power; Estimation theory; Power (physics); Telecommunications; Artificial intelligence; Physics","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":[0.001970785,0.0009078295,0.001208037,0.0006191739,0.0003238879,0.0008771619,0.000626358,0.001009692,0.0003526159],"category_scores_gemma":[0.01417602,0.0005827978,0.0003900961,0.0007761176,0.0009470982,0.001530961,0.000786328,0.0006709136,0.0001874693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004006665,"about_ca_system_score_gemma":0.0005516204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009097145,"about_ca_topic_score_gemma":0.0006035042,"domain_scores_codex":[0.9989353,0.0005300942,0.00005434183,0.0001857042,0.0002221139,0.00007249679],"domain_scores_gemma":[0.9952095,0.003490018,0.0006475133,0.0002648369,0.0003349507,0.00005322127],"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":[0.0002353599,0.0000226891,0.001666368,0.000133136,0.00006539219,0.0001441791,0.00008392922,0.9254465,0.005543059,0.01183747,0.0003564157,0.05446547],"study_design_scores_gemma":[0.00001588354,0.0000414464,0.0005634133,0.00001369548,0.00001641013,0.00007833481,0.0000151627,0.986707,0.002859721,0.009306833,0.0003619984,0.00002019526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02760962,0.000312655,0.9715214,0.00005975222,0.0000180367,0.00001395016,0.00003622659,0.00009010588,0.0003382606],"genre_scores_gemma":[0.7844681,0.001599124,0.2117856,0.00005924287,0.00017144,0.0001318516,0.0002363026,0.00005067002,0.001497726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001970785,"threshold_uncertainty_score":0.01042265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01298137311178578,"score_gpt":0.2560980657845243,"score_spread":0.2431166926727385,"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."}}