{"id":"W4392152406","doi":"10.1109/globecom54140.2023.10436815","title":"Split Learning for Sensing-Aided Single and Multi-Level Beam Selection in Multi-Vendor RAN","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Vendor; Selection (genetic algorithm); Ran; Artificial intelligence; Computer network; Business; Marketing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000132464,0.0001156853,0.0001396015,0.0002630753,0.00008109008,0.00003441397,0.00004033544,0.0001220234,0.000007360012],"category_scores_gemma":[0.0002623002,0.0001187071,0.00002872705,0.0004241425,0.00002214395,0.00008493521,0.00002318956,0.0001302485,0.00001683692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005633806,"about_ca_system_score_gemma":0.000005336071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003958176,"about_ca_topic_score_gemma":0.0003300459,"domain_scores_codex":[0.9993482,0.000009574559,0.0001737148,0.0001596656,0.00005500667,0.0002538176],"domain_scores_gemma":[0.999784,0.0000869591,0.00001691568,0.00005346058,0.00003510977,0.00002356211],"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":[0.00004287971,0.00007739246,0.01149885,0.0004284694,0.00006974456,0.00001305333,0.002072579,0.4850997,0.3041078,0.0005248871,0.001393553,0.194671],"study_design_scores_gemma":[0.001101545,0.00003072736,0.005437984,0.00002112746,0.000004897681,0.000003877314,0.0006941678,0.8850835,0.106654,0.0000471132,0.0007667709,0.0001542512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4371413,0.00004116412,0.559896,0.00004599088,0.0001231817,0.0002920595,0.00000319192,0.002343642,0.0001134268],"genre_scores_gemma":[0.9684409,0.00002806768,0.03003736,0.00001493433,0.00001769592,0.00001493339,0.00001777129,0.00003689726,0.001391393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5312996,"threshold_uncertainty_score":0.4840735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05956455999764827,"score_gpt":0.263018853613917,"score_spread":0.2034542936162688,"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."}}