{"id":"W4386494847","doi":"10.1109/usnc-ursi52151.2023.10237460","title":"Embedding General Antenna Patterns in Machine Learning Based Propagation Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Transmitter; Antenna (radio); Bandwidth (computing); Artificial neural network; Electronic engineering; Solver; Artificial intelligence; Computer engineering; Real-time computing; Channel (broadcasting); 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":[0.0002465789,0.0001223765,0.0001178891,0.0003034589,0.00004474401,0.00003623826,0.00006371203,0.00005084893,0.0001047661],"category_scores_gemma":[0.00001528095,0.0001189378,0.00003773482,0.0002776358,0.000004189105,0.0001736468,0.00002122219,0.0001828582,0.00005530755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005037774,"about_ca_system_score_gemma":0.000008428705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006797659,"about_ca_topic_score_gemma":0.00006012355,"domain_scores_codex":[0.9991832,0.0000318366,0.0002333348,0.0001631811,0.0001383316,0.0002501009],"domain_scores_gemma":[0.9997914,0.00002044325,0.00001796127,0.00009754085,0.00002498901,0.00004759661],"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":[0.000002298828,0.000004383828,0.000901972,0.00004399823,0.000004011823,0.000005651086,0.0001569635,0.9550847,0.0415681,0.00002351451,0.00001622007,0.002188196],"study_design_scores_gemma":[0.0002891795,0.00001065152,0.0001921703,0.00004021215,0.000002641699,0.000001004133,0.00004694368,0.9861884,0.01293103,0.0001208885,0.00002429909,0.0001525329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4050956,0.00001984768,0.5936358,0.0000386237,0.00006897761,0.00009265581,0.000002566371,0.0005489925,0.0004969795],"genre_scores_gemma":[0.9966016,0.00005239752,0.002652637,0.00006668478,0.0000416117,0.00002841613,0.000104624,0.00004105188,0.000410986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.591506,"threshold_uncertainty_score":0.4850141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03112130112097982,"score_gpt":0.244740586213552,"score_spread":0.2136192850925722,"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."}}