{"id":"W3101630909","doi":"10.1016/j.heliyon.2020.e05369","title":"Twin Support Vector Regression for complex millimetric wave propagation environment","year":2020,"lang":"en","type":"article","venue":"Heliyon","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Algorithm; Orthogonal frequency-division multiplexing; Multipath propagation; Channel (broadcasting); Multiplexer; Wireless; Transmission (telecommunications); Wavelet; Electronic engineering; Multiplexing; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001120824,0.0008699031,0.0008178541,0.0005960208,0.0002707862,0.0008151671,0.001005194,0.0009288255,0.001131],"category_scores_gemma":[0.00391276,0.0004265916,0.0005699052,0.0007868677,0.0004204756,0.001128501,0.0007313225,0.001499552,0.0005745142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000282615,"about_ca_system_score_gemma":0.0005639903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002288988,"about_ca_topic_score_gemma":0.001375135,"domain_scores_codex":[0.9993055,0.0002146288,0.00003621419,0.0001719323,0.0002048166,0.00006692365],"domain_scores_gemma":[0.9986742,0.0006645162,0.0001661726,0.0001082203,0.0003413183,0.00004566527],"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":[0.0001809966,0.00007072505,0.001724252,0.0001003053,0.00009123133,0.0001110831,0.00007488893,0.6696281,0.009841716,0.007297899,0.001297437,0.3095814],"study_design_scores_gemma":[0.000001628307,0.00001129846,0.00008922457,0.000001610049,0.000001990878,0.00001136236,0.000003616267,0.9985259,0.0006923198,0.0004661425,0.000191581,0.000003448623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006525215,0.0001019833,0.9929756,0.00004210605,0.00001875371,0.000007617274,0.0000141089,0.000144964,0.0001697146],"genre_scores_gemma":[0.4641401,0.000609369,0.5309571,0.0001031381,0.00008622048,0.0001064408,0.0003416547,0.0001380804,0.003517992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002288988,"threshold_uncertainty_score":0.005927563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07092909334359736,"score_gpt":0.2429250174985022,"score_spread":0.1719959241549048,"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."}}