{"id":"W2888816167","doi":"10.1109/tvt.2018.2868013","title":"Cooperative Task Scheduling for Computation Offloading in Vehicular Cloud","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":196,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Shanghai Key Laboratory of Digital Media Processing and Transmission; National Key Laboratory of Science and Technology on Communications; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Cloud computing; Computer science; Computation offloading; Distributed computing; Mobile edge computing; Scheduling (production processes); Edge computing; Job shop scheduling; Computer network; Engineering; Operating system","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":[0.0006788035,0.0006421173,0.0008838413,0.0004505066,0.0009485043,0.0008372291,0.001127004,0.0004528392,0.001110285],"category_scores_gemma":[0.001413858,0.000245783,0.0004280171,0.0008014067,0.0005249555,0.0007541135,0.0009320825,0.000524187,0.0001646771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171853,"about_ca_system_score_gemma":0.002065732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009522215,"about_ca_topic_score_gemma":0.007775837,"domain_scores_codex":[0.9993284,0.0001373999,0.00002913641,0.0001312058,0.0001324743,0.0002414278],"domain_scores_gemma":[0.9993665,0.0002445016,0.00008987467,0.00007477445,0.0001229274,0.0001015199],"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":[0.0002301036,0.00006354343,0.0006198334,0.00006678241,0.00002204909,0.0001551917,0.00010771,0.952522,0.008164094,0.01169705,0.0009553949,0.02539618],"study_design_scores_gemma":[0.000006892995,0.00002298094,0.00006728694,0.000001573093,0.00000333933,0.00001235335,0.00001943833,0.9968285,0.0006720024,0.002095487,0.0002673972,0.000002836346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1558885,0.0004553196,0.8391291,0.0002460243,0.0001040604,0.0001083156,0.00007787431,0.0002988392,0.00369209],"genre_scores_gemma":[0.975128,0.0001014226,0.02365658,0.00002845884,0.00001812171,0.00004505491,0.00004101549,0.00002170141,0.0009595915],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009522215,"threshold_uncertainty_score":0.01893359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01271137913213899,"score_gpt":0.2610528863561882,"score_spread":0.2483415072240492,"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."}}