{"id":"W3188555847","doi":"10.1109/icc42927.2021.9500269","title":"On the Performance of Deep Learning Models for Uplink CSI Prediction in Vehicular Environments","year":2021,"lang":"en","type":"article","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Convolutional neural network; Recurrent neural network; Telecommunications link; Machine learning; Process (computing); Artificial neural 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001266559,0.00005180174,0.00006036124,0.00002697543,0.00002912479,0.000005501337,0.00003219444,0.00003285437,0.00006205673],"category_scores_gemma":[0.00001238473,0.00004110755,0.00002664365,0.0000408962,0.000004920163,0.00005884927,0.00001018492,0.00009128326,0.000005492106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000245349,"about_ca_system_score_gemma":0.000002566233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.374326e-7,"about_ca_topic_score_gemma":0.00000111528,"domain_scores_codex":[0.9996225,0.00001108651,0.0001349525,0.00007638625,0.00007196829,0.00008314152],"domain_scores_gemma":[0.9998486,0.00003373451,0.00001305248,0.00008253801,0.000009464107,0.00001257857],"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":[0.000003109918,0.000007617287,0.00009656067,0.0000223613,0.000007364623,1.261635e-7,0.0001300193,0.9635229,0.0326345,0.0001672548,0.000005225714,0.003403029],"study_design_scores_gemma":[0.0001391926,0.00002126406,0.00007585382,0.00001772729,0.000003374778,3.628545e-7,0.00004395111,0.8465422,0.1528913,0.0001674991,0.00006278691,0.00003447068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5052532,0.00005302596,0.4938069,0.00001300121,0.00002638043,0.0000652772,4.57328e-7,0.00001478752,0.0007669552],"genre_scores_gemma":[0.9979968,0.0001245454,0.001546068,0.00003187458,0.00001036237,0.00002718905,0.00001095554,0.00001120288,0.0002410128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4927436,"threshold_uncertainty_score":0.1676317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01950764268109305,"score_gpt":0.1902612550495667,"score_spread":0.1707536123684737,"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."}}