{"id":"W4312283671","doi":"10.1109/tvt.2022.3228583","title":"Resource Allocation for Integrated Sensing and Communication in Digital Twin Enabled Internet of Vehicles","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Virtualization; Distributed computing; Network virtualization; Cloud computing; Computer network; Resource allocation","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":[0.0002762582,0.0000962805,0.0001559519,0.0005486153,0.0001629739,0.00003813628,0.0004089971,0.0000895783,6.625289e-7],"category_scores_gemma":[0.00001536682,0.0001067186,0.00004068637,0.0007366931,0.0001029671,0.0001412042,0.00002454631,0.0003447929,8.003972e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009042949,"about_ca_system_score_gemma":0.00003289794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004977822,"about_ca_topic_score_gemma":0.000009646333,"domain_scores_codex":[0.9991764,0.00005660407,0.0002545958,0.000246558,0.0001019518,0.0001639368],"domain_scores_gemma":[0.9993219,0.0001255307,0.00009040181,0.0003801961,0.00006453637,0.00001740717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009591473,0.0003657154,0.0001821631,0.0000473549,0.00006703415,0.000009910473,0.002343095,0.01682911,0.02247436,0.004199323,0.000325707,0.9530603],"study_design_scores_gemma":[0.0008764863,0.0003495535,0.00005411772,0.00007484436,0.00001211772,0.00007252082,0.0005548407,0.8954735,0.08872852,0.004855343,0.008743385,0.0002047508],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3733411,0.00006262035,0.625287,0.0008377209,0.0001734443,0.0001615876,9.351404e-7,0.0001151475,0.00002048108],"genre_scores_gemma":[0.9896554,0.000004772019,0.01018996,0.00005109631,0.000007638147,0.00002952513,0.000006373647,0.00000957147,0.00004566824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9528556,"threshold_uncertainty_score":0.4351856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0106954135357003,"score_gpt":0.2206289736092497,"score_spread":0.2099335600735494,"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."}}