{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001360505,0.0001074577,0.0001621794,0.00005253127,0.00002225066,0.0001161678,0.00008447525,0.0001089172,0.00000538758],"category_scores_gemma":[0.000005985602,0.0001149397,0.00003626231,0.0003191504,0.0000424216,0.0004906236,0.00001074295,0.000204424,0.000007650995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004334526,"about_ca_system_score_gemma":0.00001130981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003330677,"about_ca_topic_score_gemma":0.000005146378,"domain_scores_codex":[0.9992712,0.00002084116,0.0003352021,0.00009122836,0.0001019783,0.0001796146],"domain_scores_gemma":[0.9996538,0.00009737237,0.00002393967,0.0001641225,0.0000218833,0.00003889361],"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":[0.0000051191,0.000005641947,0.0006839868,0.00007127356,0.00003127445,0.000002082083,0.0001126263,0.9914002,0.000008315807,0.0004033764,0.005736728,0.001539358],"study_design_scores_gemma":[0.0003027714,0.00001720048,0.0007930199,0.0005865126,0.00001366127,0.000005614401,0.00002464721,0.9917508,0.00002506747,0.0009681188,0.005373814,0.0001387926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2972969,0.01994086,0.5274045,0.0006893727,0.008537096,0.00211303,0.0001713127,0.002012236,0.1418346],"genre_scores_gemma":[0.9985281,0.0001939418,0.0007608766,0.00002121035,0.0003894811,0.00002165233,0.00003548016,0.00002732361,0.00002197536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7012311,"threshold_uncertainty_score":0.4687104,"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."}}