{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000966937,0.0001580983,0.0001654279,0.00008472576,0.00006577482,0.00002439159,0.00006813028,0.00007148515,0.0003944818],"category_scores_gemma":[0.00003133432,0.0001446622,0.00007475201,0.0001311317,0.00001148625,0.00008027076,0.00002559824,0.00009617448,0.0001957523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007167616,"about_ca_system_score_gemma":0.000009436272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.313713e-7,"about_ca_topic_score_gemma":3.162373e-7,"domain_scores_codex":[0.9990819,0.00001867066,0.0002712402,0.0002235028,0.0001886116,0.0002161444],"domain_scores_gemma":[0.9996343,0.00002736344,0.00004347085,0.0001359781,0.00002560417,0.0001332868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006391794,0.00004123297,0.00007448644,0.001270476,0.00004561925,0.000006106256,0.001063053,0.01466607,0.9422348,0.00005484287,0.003019752,0.03745968],"study_design_scores_gemma":[0.0008191282,0.0002539645,0.0004515612,0.00008029499,0.00003508916,0.000003493324,0.00004742593,0.5424889,0.3887895,0.00003254975,0.0666494,0.0003486242],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03847044,0.0009144351,0.9570729,0.0007465627,0.0002255069,0.0008514951,0.00004857645,0.0003568542,0.001313244],"genre_scores_gemma":[0.9851822,0.0005001609,0.0132721,0.0003965345,0.000221087,0.00007110157,0.0001896851,0.00005170034,0.0001154742],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9467117,"threshold_uncertainty_score":0.5899152,"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."}}