{"id":"W3189163723","doi":"10.1109/icc42927.2021.9500563","title":"COrRect: Connection-Oriented Resource Matching for Vehicular Clouds","year":2021,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Vehicular ad hoc network; Cloud computing; Resource management (computing); Resource (disambiguation); Matching (statistics); Computer network; Distributed computing; Service (business); Enhanced Data Rates for GSM Evolution; Resource allocation; Wireless ad hoc network; Telecommunications; Wireless","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":[],"consensus_categories":[],"category_scores_codex":[0.0002952883,0.0001059208,0.0001363512,0.00004299225,0.0003154668,0.0001699832,0.0002943506,0.00005406605,0.00000828815],"category_scores_gemma":[0.0001019324,0.0001028018,0.00009805572,0.0003800209,0.00001220174,0.0001521721,0.0002450873,0.0001074328,0.00003274768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000354168,"about_ca_system_score_gemma":0.00007180857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002484038,"about_ca_topic_score_gemma":0.000003380619,"domain_scores_codex":[0.9989338,0.00004808598,0.0001875554,0.0003758733,0.0001552936,0.0002994207],"domain_scores_gemma":[0.9990948,0.0002526364,0.00004850893,0.0003725312,0.0001577609,0.00007379501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004869587,0.0003939603,0.002016679,0.0001833017,0.000207484,0.0002428131,0.01049018,0.004113915,0.01610969,0.3197785,0.4211954,0.2252193],"study_design_scores_gemma":[0.0006519304,0.00006810491,0.0003072202,0.00004957641,0.00001043329,0.0001224738,0.0002831109,0.284566,0.02604727,0.005968703,0.6815785,0.0003465873],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05389514,0.00008384022,0.9269602,0.001030387,0.009223014,0.0001013433,1.255359e-7,0.0003125269,0.008393366],"genre_scores_gemma":[0.4933847,0.000007895746,0.465086,0.008344023,0.01001169,0.00006183992,0.00006481951,0.00008157758,0.02295745],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4618742,"threshold_uncertainty_score":0.4192135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01325416118687479,"score_gpt":0.2406049228951253,"score_spread":0.2273507617082506,"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."}}