{"id":"W4405778577","doi":"10.1109/mnet.2024.3522588","title":"VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Network","year":2024,"lang":"en","type":"article","venue":"IEEE Network","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Guelph","funders":"","keywords":"Computer science; Joint (building); Computer network; Control (management); Distributed computing; Telecommunications; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00110133,0.0006112389,0.0007538171,0.0003003016,0.0003468212,0.0009654969,0.0007553106,0.0006946765,0.001111721],"category_scores_gemma":[0.002373474,0.0003586759,0.0002786498,0.0003132391,0.001198094,0.0009290848,0.001162138,0.0009801358,0.0001079849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001203539,"about_ca_system_score_gemma":0.001294914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006720799,"about_ca_topic_score_gemma":0.004476535,"domain_scores_codex":[0.9995283,0.0001379431,0.00001716142,0.0001001893,0.00009665887,0.0001198214],"domain_scores_gemma":[0.9992352,0.0004421102,0.0001047862,0.000032661,0.0001212751,0.00006400095],"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":[0.00003760836,0.00001166315,0.0002706005,0.00001391319,0.000009567224,0.00002965839,0.00002298843,0.9852532,0.0005255409,0.008088815,0.0001916917,0.005544787],"study_design_scores_gemma":[0.000002468686,0.000008804778,0.00003140831,0.00000112344,0.000001581433,0.000002922384,0.000004060495,0.9977193,0.0001201533,0.002038318,0.00006821617,0.000001635238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05813897,0.0002818683,0.935778,0.0003979209,0.00004594265,0.00003605884,0.00003643455,0.0001129775,0.005171791],"genre_scores_gemma":[0.9826885,0.00008186798,0.01540931,0.00005257484,0.00001480503,0.00003278808,0.00001983112,0.00001728016,0.00168306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006720799,"threshold_uncertainty_score":0.01336336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059803256818944,"score_gpt":0.2023672347824893,"score_spread":0.1917692022142999,"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."}}