{"id":"W3144845068","doi":"10.1109/tsc.2021.3070746","title":"Resource Trading in Edge Computing-Enabled IoV: An Efficient Futures-Based Approach","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Futures contract; Edge computing; Resource (disambiguation); Negotiation; Low latency (capital markets); Algorithmic trading; Distributed computing; Enhanced Data Rates for GSM Evolution; Computer network; Telecommunications; 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":[0.001947121,0.0006882843,0.001293603,0.0006376216,0.0009121573,0.001819101,0.002348368,0.001377047,0.002430364],"category_scores_gemma":[0.002692941,0.0004711171,0.0007258242,0.0009334027,0.0009611158,0.002844174,0.002125176,0.001108994,0.0002289693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001223042,"about_ca_system_score_gemma":0.001977826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004605718,"about_ca_topic_score_gemma":0.003016356,"domain_scores_codex":[0.9987947,0.0003651425,0.0000606934,0.0002000919,0.0003777909,0.0002015047],"domain_scores_gemma":[0.9991491,0.0003965412,0.00009675867,0.00009254489,0.0001739165,0.00009124436],"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":[0.0001255242,0.0000692114,0.000842383,0.00006561721,0.00004115504,0.0001971463,0.000137128,0.8818554,0.002986333,0.06319592,0.001045252,0.04943895],"study_design_scores_gemma":[0.000004751031,0.00001869577,0.00004796486,0.000003615625,0.000004990895,0.00003303164,0.00002470923,0.9861822,0.0003240197,0.0126425,0.0007071577,0.000006326929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02521385,0.0006020781,0.9684762,0.0003337989,0.00005507233,0.00006965467,0.00004750548,0.0001223353,0.005079495],"genre_scores_gemma":[0.866979,0.000371343,0.1289343,0.0001075311,0.00004681851,0.00008324211,0.00009104613,0.00005588904,0.003330801],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004605718,"threshold_uncertainty_score":0.01029748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01384344171392852,"score_gpt":0.2321601459057042,"score_spread":0.2183167041917757,"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."}}