{"id":"W4364321640","doi":"10.1109/jiot.2023.3265655","title":"IRS-Assisted High-Speed Train Communications: Performance Analysis and Optimal Configuration","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; Toyota Motor Corporation; National Natural Science Foundation of China; Royal Society; Amazon Catalyst; U.S. Department of Transportation; National Science Foundation","keywords":"Computer science; Computer network; Telecommunications","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":[0.0009975211,0.001285058,0.0009338466,0.0005195831,0.0005968525,0.001196494,0.000908282,0.001108818,0.002092757],"category_scores_gemma":[0.002999314,0.0004996834,0.0004701373,0.001002372,0.001148979,0.001346932,0.001426412,0.0006632781,0.000522543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001584955,"about_ca_system_score_gemma":0.001450337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004596006,"about_ca_topic_score_gemma":0.003765863,"domain_scores_codex":[0.9990185,0.0004106394,0.00002673138,0.0001201225,0.0001983389,0.000225711],"domain_scores_gemma":[0.9985703,0.0005913328,0.0002879876,0.0001039508,0.0003475829,0.00009880288],"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":[0.0001391286,0.00004339965,0.0009046067,0.00006475935,0.00003697397,0.0001140269,0.0000529763,0.976702,0.003619435,0.005711489,0.0008227964,0.01178844],"study_design_scores_gemma":[0.00001089765,0.00006691884,0.0002420068,0.000005595495,0.00001444135,0.00004843073,0.00004237454,0.9968951,0.001169893,0.001317876,0.0001750927,0.00001136727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2954159,0.001817596,0.6712943,0.001068423,0.00008996797,0.0001671258,0.0002132473,0.0005657251,0.02936772],"genre_scores_gemma":[0.979094,0.0003722806,0.01908904,0.00005377869,0.00002823732,0.00005203673,0.00005519702,0.00002025363,0.001235143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004596006,"threshold_uncertainty_score":0.01149976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02630944679925898,"score_gpt":0.2643049538787838,"score_spread":0.2379955070795248,"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."}}