{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002327932,0.0002048648,0.0002259368,0.0001355262,0.0002069572,0.0001305956,0.0004291298,0.0001088124,0.000005458709],"category_scores_gemma":[0.000001819691,0.0002476842,0.00007784013,0.000367619,0.00005619541,0.0001001961,0.0001833652,0.0002798198,0.000008862543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001349331,"about_ca_system_score_gemma":0.00001293857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005630776,"about_ca_topic_score_gemma":0.00005622085,"domain_scores_codex":[0.9987731,0.0001509756,0.0003495749,0.0002976341,0.0001337283,0.0002949954],"domain_scores_gemma":[0.9984336,0.0002351194,0.00004882657,0.001138919,0.00005816384,0.00008532489],"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":[0.00001433318,0.0001136276,0.00002075139,0.00002560436,0.00008698564,0.000006645608,0.0001898446,0.9620705,0.000635385,0.006809468,0.004746879,0.02528003],"study_design_scores_gemma":[0.0007287405,0.00005766334,0.0002688695,0.0001938843,0.00003753848,0.00001313643,0.0001196325,0.9738464,0.0002520735,0.00008486365,0.02414628,0.0002509198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05316314,0.003413298,0.9222164,0.005765034,0.001235813,0.001543751,0.00003025813,0.0003661436,0.01226619],"genre_scores_gemma":[0.9811108,0.002983391,0.01425063,0.0004976462,0.0001613187,0.0002193599,0.0005560272,0.00005217782,0.0001686408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9279477,"threshold_uncertainty_score":0.9999976,"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."}}