{"id":"W1498687686","doi":"10.1109/vetec.1993.507542","title":"Narrow-band small area microcellular mobile radio channel characterization using ray tracing technique","year":2002,"lang":"en","type":"article","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Nakagami distribution; Ray tracing (physics); Uniform theory of diffraction; Narrowband; Diffraction; Shadow mapping; Rayleigh distribution; Computer science; Radio propagation; Enhanced Data Rates for GSM Evolution; Tracing; Rayleigh scattering; Electronic engineering; Channel (broadcasting); Optics; Telecommunications; Physics; Engineering; Fading; Artificial intelligence","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":[0.00006100606,0.0001418668,0.0001592395,0.0002460287,0.0001183501,0.0002346433,0.0001572195,0.000148614,0.000681819],"category_scores_gemma":[0.0002918396,0.00006713589,0.0001202874,0.0002837632,0.0001695252,0.0002075642,0.0001050252,0.0001213182,0.0001583442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000218079,"about_ca_system_score_gemma":0.0002435515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001740142,"about_ca_topic_score_gemma":0.001595468,"domain_scores_codex":[0.9999481,0.000007500597,0.000001562,0.000007004077,0.00002700152,0.000008798639],"domain_scores_gemma":[0.9998573,0.00007111393,0.00001911309,0.00002190019,0.00002383441,0.000006725048],"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":[0.0001042462,0.00005316906,0.004262629,0.00005512223,0.00002041662,0.0002002116,0.0001348623,0.7528874,0.1793744,0.008397799,0.0004592226,0.05405052],"study_design_scores_gemma":[0.000002616915,0.00004144982,0.001521115,0.00000200915,0.000004148986,0.00008083633,0.00001830292,0.957232,0.03931997,0.000839166,0.0009296843,0.000008640107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4092885,0.0001439918,0.5850645,0.00004603738,0.00001165049,0.00004164083,0.0001433872,0.001008189,0.004252127],"genre_scores_gemma":[0.9552225,0.000108134,0.04310447,0.000004863466,0.00000266393,0.00001940396,0.00006654074,0.00002434945,0.001447094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001740142,"threshold_uncertainty_score":0.00346005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03679483032888426,"score_gpt":0.191976666629994,"score_spread":0.1551818363011098,"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."}}