{"id":"W2568655342","doi":"10.1109/tap.2016.2647589","title":"A Gaussian Beam Approximation Approach for Embedding Antennas Into Vector Parabolic Equation-Based Wireless Channel Propagation Models","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discretization; Solver; Gaussian; Computer science; Antenna (radio); Overhead (engineering); Gaussian beam; Beam (structure); Wireless; Algorithm; Mathematical optimization; Physics; Mathematics; Mathematical analysis; Optics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00052818,0.000556595,0.0004118935,0.0004170298,0.0002530683,0.0007557274,0.0009276218,0.0009912835,0.001868728],"category_scores_gemma":[0.001067186,0.0003805315,0.0007238818,0.000624876,0.0005748908,0.0008092013,0.0009190439,0.001120029,0.0009253959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004489783,"about_ca_system_score_gemma":0.0007941088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002521328,"about_ca_topic_score_gemma":0.001847619,"domain_scores_codex":[0.999781,0.00007269606,0.00001098032,0.00002148463,0.00009049027,0.00002344722],"domain_scores_gemma":[0.9996898,0.0001354188,0.00003002617,0.00004211056,0.00008636028,0.00001619968],"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":[0.00003862679,0.0000450386,0.0006529556,0.00007632562,0.00002247076,0.0001686232,0.0001557375,0.8515,0.01221274,0.1069192,0.001564192,0.0266442],"study_design_scores_gemma":[0.000003130305,0.00001163705,0.00003583104,0.000006271374,0.000002856485,0.00003143877,0.00001186804,0.9934205,0.001011229,0.003763123,0.001696063,0.00000599969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002413357,0.00007884159,0.9949808,0.00005820925,0.0000184114,0.00001479815,0.00003165691,0.00009667678,0.002307299],"genre_scores_gemma":[0.275691,0.001379106,0.7082617,0.0002362329,0.00005982377,0.0002962801,0.0002442217,0.0001951835,0.01363645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002521328,"threshold_uncertainty_score":0.006251514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04454745375745476,"score_gpt":0.2615443414572927,"score_spread":0.216996887699838,"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."}}