{"id":"W4383899703","doi":"10.1109/jiot.2023.3294400","title":"Collaborative Edge Intelligence Service Provision in Blockchain Empowered Urban Rail Transit Systems","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Beijing Jiaotong University; Natural Science Foundation of Beijing Municipality; Beijing Municipal Education Commission; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; China Railway","keywords":"Computer science; Edge computing; Computer security; Service (business); Distributed computing; Artificial intelligence; Computer network; Enhanced Data Rates for GSM Evolution","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001063987,0.0004710839,0.0007017466,0.0003742336,0.001025922,0.001289653,0.001069455,0.0008431893,0.003560702],"category_scores_gemma":[0.00214638,0.0002143138,0.0002998104,0.0005545621,0.0007900519,0.001786848,0.002052273,0.0007076269,0.0004450757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001169912,"about_ca_system_score_gemma":0.001871107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008161743,"about_ca_topic_score_gemma":0.007048838,"domain_scores_codex":[0.9990195,0.000261944,0.00005331783,0.000192241,0.0002230003,0.0002500073],"domain_scores_gemma":[0.9989889,0.000374624,0.0001509244,0.0001407645,0.0002016901,0.0001430772],"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":[0.0002847142,0.0001168663,0.001887091,0.00007222717,0.00002383481,0.0004526511,0.000233089,0.9356667,0.00382094,0.02447732,0.001074039,0.03189046],"study_design_scores_gemma":[0.00001202565,0.00002438631,0.00008830061,0.000003282659,0.000003246167,0.00002096275,0.00002265843,0.9946159,0.0003942246,0.004269144,0.000541724,0.000004197436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.311063,0.0004903114,0.6705065,0.000863832,0.0000798254,0.0002670275,0.0002078064,0.0009114039,0.01561017],"genre_scores_gemma":[0.9929061,0.00006199029,0.005689058,0.00002061348,0.000007553863,0.00003495607,0.00004001096,0.000008543429,0.001231193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008161743,"threshold_uncertainty_score":0.0162285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0251458577991482,"score_gpt":0.2806832238303273,"score_spread":0.2555373660311792,"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."}}