{"id":"W4403182013","doi":"10.23919/jcin.2024.10707104","title":"Digital-Twin Enabled Time Ahead Resource Allocation for Integrated Fiber-Wireless Connected Vehicular Network","year":2024,"lang":"en","type":"article","venue":"Journal of Communications and Information Networks","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Computer network; Wireless; Wireless network; Resource (disambiguation); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004115606,0.0003628581,0.0003443461,0.0004241463,0.0004565857,0.0004204758,0.000642016,0.0002843167,0.0007854791],"category_scores_gemma":[0.0009064404,0.0001190749,0.000165683,0.0003761994,0.000281934,0.0008641256,0.0005416803,0.0003098947,0.0001138653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006490784,"about_ca_system_score_gemma":0.0006989744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005369086,"about_ca_topic_score_gemma":0.008772181,"domain_scores_codex":[0.9997999,0.00004286034,0.000007574813,0.00004144973,0.00003689204,0.00007122965],"domain_scores_gemma":[0.99975,0.00009888471,0.00002659284,0.00002852223,0.0000603614,0.00003571761],"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":[0.0004013858,0.0001714186,0.001999367,0.00005579324,0.00003016276,0.0001287791,0.0001042947,0.8141672,0.01937043,0.007604346,0.001643654,0.1543232],"study_design_scores_gemma":[0.000002812548,0.00004285539,0.0001464303,0.000001469074,0.000003876884,0.0000217166,0.00001705608,0.9967981,0.001723662,0.0009653165,0.0002728611,0.000003880384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2887975,0.0005394972,0.7057915,0.0002032017,0.0000802924,0.0000543692,0.00006751788,0.000525603,0.003940724],"genre_scores_gemma":[0.9748575,0.0000610645,0.02433193,0.0000213414,0.000005043513,0.00001275084,0.00003695261,0.000008913225,0.0006644809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005369086,"threshold_uncertainty_score":0.01067567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00916713854530888,"score_gpt":0.2141469257767682,"score_spread":0.2049797872314593,"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."}}