{"id":"W4403024465","doi":"10.1109/pacrim61180.2024.10690195","title":"Learning-Based Ultra-Wideband Indoor NLOS Identification","year":2024,"lang":"en","type":"article","venue":"","topic":"Ultra-Wideband Communications Technology","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Non-line-of-sight propagation; Identification (biology); Computer science; Wideband; Telecommunications; Electronic engineering; Wireless; 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":[0.0005488798,0.0008093621,0.0007058342,0.0007723093,0.0003517292,0.0006353142,0.0008939693,0.0006121431,0.001147455],"category_scores_gemma":[0.002012263,0.0002421328,0.000380838,0.0004702631,0.0003959815,0.001142609,0.001070476,0.0008296762,0.001429621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003261247,"about_ca_system_score_gemma":0.0004627059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001518494,"about_ca_topic_score_gemma":0.00241177,"domain_scores_codex":[0.9993837,0.0000879252,0.00002549226,0.0001722622,0.0002042664,0.0001262336],"domain_scores_gemma":[0.9991977,0.0002459949,0.0001422895,0.000117578,0.000252209,0.00004431819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000295099,0.0002157489,0.009476563,0.0001447064,0.00008729464,0.0002847169,0.0001335661,0.2656196,0.0524516,0.001666343,0.002600264,0.6670244],"study_design_scores_gemma":[0.000006998603,0.00006525323,0.002862766,0.00001407363,0.00002640034,0.0001959212,0.00005427076,0.9766466,0.01775979,0.001100123,0.001245215,0.00002258604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09442551,0.0003687239,0.8973255,0.0001395626,0.0001253328,0.00003753995,0.0001059736,0.002470866,0.005001002],"genre_scores_gemma":[0.8721486,0.0002872128,0.1207015,0.0002601679,0.00009531221,0.00005450057,0.000440907,0.0001338977,0.005877852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001518494,"threshold_uncertainty_score":0.003838599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006953107626597671,"score_gpt":0.2196355761355221,"score_spread":0.2126824685089244,"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."}}