{"id":"W3085286216","doi":"10.1109/access.2020.3019907","title":"IEEE Access Special Section Editorial: Advances in Statistical Channel Modeling for Future Wireless Communications Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Wireless; Rician fading; Computer science; Channel (broadcasting); Fading; MIMO; Nakagami distribution; Electronic engineering; Wireless network; Communications system; Telecommunications; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001711974,0.00020004,0.0002644586,0.00009337163,0.0001495036,0.0002347402,0.0007306706,0.0001850722,0.00001542666],"category_scores_gemma":[0.00002819743,0.0002173572,0.00005216452,0.0003092579,0.0000247078,0.001114359,0.00007859169,0.0004431937,0.000003298372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000775627,"about_ca_system_score_gemma":0.00002735853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002387801,"about_ca_topic_score_gemma":0.0002895249,"domain_scores_codex":[0.9987469,0.00004572708,0.00042755,0.0002754327,0.0001938912,0.0003104394],"domain_scores_gemma":[0.9992893,0.0001145784,0.0000599089,0.0002728908,0.0001372032,0.0001260915],"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":[0.0000559649,0.00001978007,0.00002103348,0.0001101718,0.0000141204,7.22186e-7,0.0003375331,0.9828745,0.0003461025,0.00006424839,0.008991388,0.007164476],"study_design_scores_gemma":[0.0005126808,0.00002199621,0.000006528723,0.00003783977,0.00001756516,5.986254e-7,0.00009405855,0.9914381,0.001050998,0.0005319006,0.006038078,0.0002496351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01038373,0.0004388664,0.9402328,0.0002623757,0.04770625,0.0004602183,0.00005571131,0.0002116832,0.0002484158],"genre_scores_gemma":[0.8679199,0.001849914,0.00111732,0.0001793615,0.128619,0.0001695806,0.00008709657,0.00005578254,0.000002021283],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9391154,"threshold_uncertainty_score":0.8863568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05710449088737677,"score_gpt":0.3192748007275271,"score_spread":0.2621703098401503,"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."}}