{"id":"W1562957666","doi":"10.1109/icc.1995.524227","title":"Performance of north American digital cellular system with frequency-offset diversity and differential detection","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Viterbi decoder; Computer science; Frequency offset; Bit error rate; Fading; Viterbi algorithm; Rayleigh fading; Decoding methods; Additive white Gaussian noise; Electronic engineering; Algorithm; Diversity scheme; Transmitter; Convolutional code; Transmit diversity; Telecommunications; Channel (broadcasting); Orthogonal frequency-division multiplexing; Engineering","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":[0.000615498,0.0005616616,0.0005178561,0.0004059683,0.0004161413,0.0006112701,0.0004024632,0.0007111087,0.001323016],"category_scores_gemma":[0.002082039,0.0001832694,0.0002284758,0.0006409398,0.0006493144,0.0004990601,0.0004521932,0.0002296306,0.0002437354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007482882,"about_ca_system_score_gemma":0.0006434363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009947195,"about_ca_topic_score_gemma":0.008284532,"domain_scores_codex":[0.9992747,0.0002538218,0.00003608256,0.00007731728,0.0001870323,0.0001711041],"domain_scores_gemma":[0.9977039,0.001161595,0.0001738339,0.0001378325,0.0007473945,0.0000754403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001876683,0.0001268411,0.01279361,0.000204514,0.0001646892,0.0006082234,0.0002229362,0.9091888,0.04047272,0.002923685,0.0006362817,0.03078095],"study_design_scores_gemma":[0.00006704434,0.001112785,0.007157205,0.0000319605,0.0001927557,0.0005184561,0.0001740053,0.9575757,0.03144937,0.0008283084,0.0008332384,0.00005926948],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843427,0.0003690019,0.01029796,0.0001269467,0.00001687595,0.00001412993,0.0000777345,0.0001774255,0.004577221],"genre_scores_gemma":[0.9980115,0.00009190333,0.00118279,0.00002275305,0.000003765492,0.000005662164,0.00005427061,0.000005471819,0.0006218167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009947195,"threshold_uncertainty_score":0.01977855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006802292990934674,"score_gpt":0.155466023057943,"score_spread":0.1486637300670083,"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."}}