{"id":"W4385848828","doi":"10.1109/tvt.2023.3304856","title":"Symbol-Level Integrated Sensing and Communication Enabled Multiple Base Stations Cooperative Sensing","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Demodulation; Base station; Computer science; Synchronization (alternating current); Sensor fusion; Real-time computing; Electronic engineering; Carrier-to-noise ratio; Communications system; Signal-to-noise ratio (imaging); Engineering; Telecommunications; Artificial intelligence; Channel (broadcasting)","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":[0.0004887874,0.0005359815,0.0005247341,0.0004334542,0.0003263639,0.0006775196,0.0009380101,0.0007430221,0.0007333873],"category_scores_gemma":[0.001067271,0.0002489421,0.0004624224,0.0005583426,0.0004753089,0.001077182,0.001204749,0.0005828353,0.0004008561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003616847,"about_ca_system_score_gemma":0.0005149649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009210022,"about_ca_topic_score_gemma":0.0008740187,"domain_scores_codex":[0.9990324,0.0001871523,0.00003676784,0.0002147489,0.0004126937,0.0001162256],"domain_scores_gemma":[0.9993405,0.0001558527,0.00008437243,0.0001379442,0.0002423214,0.00003899826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004397667,0.0001369081,0.00355025,0.0003379645,0.0001507894,0.0005070163,0.0005888064,0.1963972,0.4051973,0.02458048,0.002890467,0.365223],"study_design_scores_gemma":[0.00004223417,0.0003955529,0.001540933,0.00002249228,0.00006742594,0.0003827656,0.00008941221,0.9091091,0.07455818,0.006337582,0.007388096,0.00006627895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05302897,0.0006471109,0.9408481,0.0001839203,0.0001109095,0.00004663201,0.00004206063,0.0006453905,0.004446932],"genre_scores_gemma":[0.8552023,0.0003471558,0.141757,0.0001916877,0.00008340889,0.00008030662,0.00009273981,0.00002809208,0.002217339],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009380101,"threshold_uncertainty_score":0.002624214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01848902317872592,"score_gpt":0.2263800243908561,"score_spread":0.2078910012121302,"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."}}