{"id":"W4283736533","doi":"10.36227/techrxiv.19433765.v3","title":"Efficient Propagation Modeling for Communication Channels with Reconfigurable Intelligent Surfaces","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transmitter; Ray tracing (physics); Radio propagation; Computer science; Radar cross-section; Wave propagation; Radar; Electronic engineering; Acoustics; Telecommunications; Engineering; Physics; Optics; Channel (broadcasting)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003215949,0.0002823826,0.0003047081,0.0001937099,0.0002304572,0.00007796386,0.001011184,0.0001783072,0.00007854309],"category_scores_gemma":[0.00004269209,0.0002692326,0.00006540323,0.0001732121,0.00004949589,0.00005191465,0.0004653339,0.0007364011,0.000004761788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003740397,"about_ca_system_score_gemma":0.0000434447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003683553,"about_ca_topic_score_gemma":0.00002560849,"domain_scores_codex":[0.9988053,0.00004328344,0.0004193223,0.0003062936,0.0001858567,0.000239965],"domain_scores_gemma":[0.9979216,0.0001303653,0.0001399683,0.001605485,0.0001731154,0.00002950097],"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":[0.00001287739,0.00002611896,0.000002522331,0.0002227066,0.00004711826,8.15004e-8,0.0002978378,0.9903278,0.0001844687,0.002340732,0.00004465574,0.006493029],"study_design_scores_gemma":[0.0001187047,0.00003293347,6.995054e-7,0.0001444412,0.00001370595,8.901997e-7,0.001399509,0.9806949,0.01294721,0.003461707,0.000845859,0.0003393857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05863729,0.002696051,0.9311734,0.0003329408,0.0002163306,0.001832342,0.00003223332,0.002388261,0.002691175],"genre_scores_gemma":[0.9164112,0.001687655,0.07877047,0.000009533949,0.000009339486,0.002482285,0.0003694232,0.00008249372,0.0001776124],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8577739,"threshold_uncertainty_score":0.999976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04181311793855327,"score_gpt":0.2665973097810079,"score_spread":0.2247841918424547,"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."}}