{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008246353,0.0001592391,0.00015523,0.0004633868,0.0001615953,0.00001652659,0.0001441024,0.0002483979,0.00001241385],"category_scores_gemma":[0.000002798229,0.0001720096,0.00008765388,0.0002672098,0.00003033093,0.0001313567,3.170275e-7,0.0002733578,0.00001682055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007035598,"about_ca_system_score_gemma":0.00001837636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.361354e-7,"about_ca_topic_score_gemma":0.000003550572,"domain_scores_codex":[0.9991744,0.0000106252,0.0002234614,0.0002397921,0.00009989248,0.0002518103],"domain_scores_gemma":[0.9995605,0.00002019697,0.00001975976,0.0002700578,0.00006384809,0.00006564907],"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":[0.000009148414,0.00007212558,0.000001162549,0.000007956613,0.00001886478,0.000002001537,0.00009867383,0.8687,0.08629631,0.0001034883,0.00004827666,0.04464195],"study_design_scores_gemma":[0.0002946632,0.0001394557,0.000002851812,0.00001007116,0.0000253686,0.00001436045,0.00004419776,0.8678668,0.1280572,0.003274103,0.0001020161,0.0001688531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1228212,0.00004367981,0.8750852,0.0005237554,0.0001144346,0.0002442106,0.00002131153,0.001050386,0.00009579856],"genre_scores_gemma":[0.9497752,0.00003196265,0.0497545,0.0002735033,0.00001770259,0.00007146628,0.00001040476,0.00002984096,0.00003544023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.826954,"threshold_uncertainty_score":0.701435,"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."}}