{"id":"W4200222969","doi":"10.1109/iscc53001.2021.9631536","title":"Virtual Resource Composition for Allocation Management in VCC Networks","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Symposium on Computers and Communications (ISCC)","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cluster analysis; Matching (statistics); Resource management (computing); Service (business); Task (project management); Resource allocation; Resource (disambiguation); Process (computing); Wireless ad hoc network; Distributed computing; Cloud computing; Vehicular ad hoc network; Database; Computer network; Artificial intelligence; Engineering; Telecommunications; Systems engineering; Business","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001713933,0.0007992267,0.0008911068,0.001270215,0.001533492,0.002198939,0.00183394,0.000752153,0.002716478],"category_scores_gemma":[0.004058341,0.000356245,0.0003194852,0.00148381,0.001025004,0.001730654,0.00167852,0.0007535485,0.0004117313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001988766,"about_ca_system_score_gemma":0.00258406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01052953,"about_ca_topic_score_gemma":0.009852963,"domain_scores_codex":[0.9984666,0.0003655971,0.00007376644,0.0003156911,0.0003390681,0.0004392599],"domain_scores_gemma":[0.9985878,0.0005192609,0.0001178095,0.0002589576,0.0003313799,0.0001846861],"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":[0.0002883058,0.0001517611,0.001167956,0.00008951576,0.00005917759,0.0001469388,0.0001804246,0.787304,0.006696817,0.0343775,0.00458545,0.1649521],"study_design_scores_gemma":[0.000007272117,0.00002477908,0.0001172923,0.000007045373,0.000008685002,0.00004115819,0.00004118096,0.9907512,0.001336806,0.005819878,0.001835327,0.000009382832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07319307,0.000855565,0.912806,0.0003013117,0.000234291,0.0002782164,0.0001229973,0.001237542,0.01097107],"genre_scores_gemma":[0.9163973,0.0001878285,0.08026151,0.0001401802,0.00004804259,0.0001473827,0.0001245779,0.00008625058,0.00260703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01052953,"threshold_uncertainty_score":0.02093643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01008361663611702,"score_gpt":0.2210801227926834,"score_spread":0.2109965061565664,"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."}}