{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003601884,0.0005794013,0.0009464587,0.001064762,0.001200346,0.004861114,0.001594476,0.002291118,0.005119574],"category_scores_gemma":[0.00858103,0.0004606814,0.0007020439,0.003334474,0.002538759,0.01262317,0.003370898,0.002474064,0.001651754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001619678,"about_ca_system_score_gemma":0.00307831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00353163,"about_ca_topic_score_gemma":0.003005293,"domain_scores_codex":[0.9964921,0.001577962,0.0001933167,0.0004337731,0.0008198834,0.0004829511],"domain_scores_gemma":[0.9952517,0.00280921,0.0002636506,0.0007371446,0.0006402139,0.0002981678],"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":[0.0001618534,0.0001451644,0.003730969,0.001808208,0.00008519674,0.0003892237,0.0009568875,0.03212991,0.001716387,0.6695441,0.01022341,0.2791087],"study_design_scores_gemma":[0.0000391683,0.0001553101,0.001139874,0.001566506,0.00007294365,0.0005476631,0.001256246,0.1357313,0.002651556,0.7052737,0.151465,0.0001007199],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0624323,0.09683853,0.6848202,0.04746796,0.001315848,0.0004129379,0.0004917171,0.001212109,0.1050084],"genre_scores_gemma":[0.7644229,0.09036566,0.1193456,0.003830636,0.001461459,0.0003615145,0.0008387215,0.0002774988,0.01909593],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005119574,"threshold_uncertainty_score":0.01904881,"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."}}