{"id":"W2158108008","doi":"10.1109/tvt.2008.924988","title":"An Empirical Model for Nonstationary Ricean Fading","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Fading; Fading distribution; Envelope (radar); Autoregressive model; Autocorrelation; Weibull fading; Channel state information; Channel (broadcasting); Statistical physics; Computer science; Mathematics; Statistics; Algorithm; Telecommunications; Physics; Rayleigh fading; Wireless","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":[0.001382441,0.0006224108,0.0005509247,0.0006761624,0.0003033708,0.0008438986,0.00133696,0.001145067,0.00166448],"category_scores_gemma":[0.005877911,0.0003980535,0.0005069335,0.00106323,0.0007821408,0.001905922,0.0004511169,0.00137691,0.0009888343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006938293,"about_ca_system_score_gemma":0.0006091903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004576486,"about_ca_topic_score_gemma":0.003316494,"domain_scores_codex":[0.9992068,0.0001738181,0.00004197469,0.0002225857,0.0002471551,0.0001077256],"domain_scores_gemma":[0.9971347,0.001367909,0.0004876254,0.0004409662,0.0005223858,0.00004639601],"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":[0.00007575982,0.00009104289,0.008119775,0.00008616562,0.00005672672,0.0003513726,0.0002709651,0.8893068,0.004772421,0.06797379,0.002433224,0.02646197],"study_design_scores_gemma":[0.00001128825,0.00006276085,0.002901121,0.00001245256,0.00001539108,0.0002213921,0.00004245923,0.9825155,0.00062523,0.01160632,0.001960703,0.00002541135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1238367,0.0004001022,0.867358,0.0005065593,0.00007554371,0.0001033607,0.0005414595,0.0006414239,0.006536723],"genre_scores_gemma":[0.9468868,0.001033451,0.04241219,0.000206421,0.000109437,0.0002276588,0.0009571438,0.0001120086,0.008054821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004576486,"threshold_uncertainty_score":0.009099662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02533673815049232,"score_gpt":0.2807794254653575,"score_spread":0.2554426873148652,"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."}}