{"id":"W2151850978","doi":"10.1109/twc.2009.080087","title":"A packet-level model for UWB channel with people shadowing process based on angular spectrum analysis","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Ultra-Wideband Communications Technology","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Network packet; Ultra-wideband; Channel (broadcasting); Wireless; Power delay profile; Transmission (telecommunications); Real-time computing; Computer network; Bandwidth (computing); Quality of service; Fading; Electronic engineering; Delay spread; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001618395,0.0003466746,0.0004432591,0.0009269083,0.0007418642,0.00006203503,0.001545325,0.0002027603,0.00001336576],"category_scores_gemma":[0.000006774456,0.0003564125,0.000263968,0.002163108,0.0001146003,0.0001975676,0.00000322032,0.0006444804,0.00001034242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001788861,"about_ca_system_score_gemma":0.00007546884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003611992,"about_ca_topic_score_gemma":0.003240847,"domain_scores_codex":[0.9985434,0.00005966043,0.0004112343,0.0003340069,0.0002305341,0.0004212073],"domain_scores_gemma":[0.9956896,0.0003237068,0.00008390092,0.003655111,0.000139856,0.0001078133],"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":[0.00004611398,0.0005895146,0.00001782983,0.00002138437,0.0002974377,2.389612e-7,0.0007881111,0.9909201,0.0005015305,0.0007945419,0.00002006768,0.006003091],"study_design_scores_gemma":[0.0006638156,0.0001756521,0.0002157168,0.00005891492,0.0004908196,0.000002716943,0.0002404265,0.9896378,0.006943682,0.001136589,0.00003024458,0.0004036024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02446259,0.00007428953,0.9692991,0.003606449,0.00003080731,0.0006798432,0.000248137,0.001028118,0.0005706729],"genre_scores_gemma":[0.9804528,0.0001646739,0.01782441,0.0002565591,0.000007399912,0.001037194,0.0001192942,0.00006975744,0.00006794241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9559902,"threshold_uncertainty_score":0.9998888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02447939788259103,"score_gpt":0.2529524657854187,"score_spread":0.2284730679028277,"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."}}