{"id":"W4285117925","doi":"10.1109/comst.2022.3189962","title":"Integrating Edge Intelligence and Blockchain: What, Why, and How","year":2022,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; China Postdoctoral Science Foundation; Science, Technology and Innovation Commission of Shenzhen Municipality; National Research Foundation Singapore; National Natural Science Foundation of China; Singapore University of Technology and Design","keywords":"Scalability; Cloud computing; Context (archaeology); Computer science; Data science; Incentive; Enhanced Data Rates for GSM Evolution; Relevance (law); Blockchain; Protocol (science); Knowledge management; Computer security; Artificial intelligence; Political science; Database","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005155869,0.000188273,0.0002788343,0.0002017572,0.001616861,0.0004897872,0.002931001,0.0001010957,0.000009109115],"category_scores_gemma":[0.0002661633,0.0002017318,0.00004130098,0.0008470347,0.0005111867,0.0003637058,0.002850564,0.0005965414,0.000003328887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006897042,"about_ca_system_score_gemma":0.00008402635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001887664,"about_ca_topic_score_gemma":0.0002486175,"domain_scores_codex":[0.9967123,0.001996956,0.000320074,0.0004865729,0.0002180368,0.000266076],"domain_scores_gemma":[0.9952517,0.001355918,0.0002212922,0.002904394,0.0001755447,0.00009112406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002462041,0.0002341548,0.0006873934,0.00001564531,0.00004898834,0.000002327945,0.003958439,0.0000405192,0.001796948,0.7070916,0.002089042,0.2840325],"study_design_scores_gemma":[0.0009949553,0.0005896094,0.002496986,0.0001195893,0.00007932622,0.0003795066,0.01196531,0.1675515,0.007484531,0.2490454,0.557201,0.002092242],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06862145,0.01693912,0.8489133,0.06015395,0.002581185,0.001230235,0.00005826556,0.0009143479,0.000588208],"genre_scores_gemma":[0.9712733,0.00166273,0.02607253,0.0003235194,0.00007281142,0.0004674215,0.00001133421,0.00001388966,0.0001024452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9026518,"threshold_uncertainty_score":0.9996829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04905194182412049,"score_gpt":0.2831313556745745,"score_spread":0.2340794138504541,"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."}}