{"id":"W4389271838","doi":"10.46620/ursigass.2023.1475.hsko1928","title":"Efficient Uncertainty Quantification of Deterministic Wireless Channel Models Using Polynomial Chaos Expansion","year":2023,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Polynomial chaos; Wireless; Computer science; Channel (broadcasting); Uncertainty quantification; CHAOS (operating system); Polynomial; Polynomial expansion; Algorithm; Statistical physics; Mathematics; Telecommunications; Statistics; Physics; Monte Carlo method; Machine learning; Mathematical analysis; Computer security","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":[0.001600974,0.0008122764,0.0008038664,0.0005893352,0.0003752015,0.000917584,0.0007571299,0.0006945868,0.0006487527],"category_scores_gemma":[0.005389895,0.0004732557,0.0007885593,0.0005653218,0.0009353734,0.001417953,0.001465616,0.00116816,0.0001605783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008032711,"about_ca_system_score_gemma":0.001233138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002849482,"about_ca_topic_score_gemma":0.001852581,"domain_scores_codex":[0.9990515,0.0003678974,0.00003715005,0.00008812376,0.0003836667,0.00007165143],"domain_scores_gemma":[0.9973583,0.001974019,0.0002354775,0.0001669412,0.0002257353,0.00003949881],"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":[0.00001823694,0.000007520463,0.0001286984,0.00002460112,0.00001150695,0.00002355342,0.00002150003,0.9777664,0.001609802,0.01274173,0.0001204638,0.007526096],"study_design_scores_gemma":[7.726515e-7,0.000004948529,0.00002116248,0.000001719478,0.000001120753,0.00000635096,0.000001932188,0.9971897,0.0003769983,0.002317588,0.00007455567,0.000003184947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007459748,0.00006038657,0.9917665,0.00005301626,0.000005076044,0.00001274475,0.00002637847,0.0000749043,0.0005411876],"genre_scores_gemma":[0.8064713,0.0005867879,0.1905171,0.00006477663,0.00004243794,0.0001520619,0.0002102611,0.00009795074,0.001857435],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002849482,"threshold_uncertainty_score":0.008466899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2160453271117972,"score_gpt":0.3597819085746029,"score_spread":0.1437365814628057,"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."}}