{"id":"W4224313663","doi":"10.36227/techrxiv.19634610.v1","title":"Integrating Edge Intelligence and Blockchain: What, Why, and How","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Scalability; Cloud computing; Context (archaeology); Computer science; Incentive; Blockchain; Data science; Enhanced Data Rates for GSM Evolution; Relevance (law); Protocol (science); Edge computing; Knowledge management; Computer security; Artificial intelligence; Political science","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":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0005693276,0.0002832964,0.0003049186,0.0002165426,0.0003512108,0.0008491478,0.00143516,0.0003078639,0.00003641775],"category_scores_gemma":[0.00005584324,0.0002656791,0.00004981137,0.0003045954,0.0002398907,0.0001546795,0.008237109,0.001254272,0.000001585865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004048082,"about_ca_system_score_gemma":0.00006591495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008251501,"about_ca_topic_score_gemma":0.00008395549,"domain_scores_codex":[0.9982257,0.00008431755,0.0002069225,0.001033629,0.0001821381,0.0002673202],"domain_scores_gemma":[0.9983548,0.0001591743,0.0001592267,0.001155267,0.00007569787,0.00009584574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[8.195792e-7,0.00003736093,0.0001751915,0.00005927914,0.00002342223,0.000007395969,0.001241063,0.0000260084,0.00002153569,0.6810229,0.0008138222,0.3165711],"study_design_scores_gemma":[0.0001192575,0.0001327086,0.0001688576,0.0001384771,0.00002339345,0.0001397389,0.004705428,0.4935217,0.001339455,0.4629045,0.03587338,0.0009330614],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0225121,0.006633337,0.9316636,0.03649389,0.0004463964,0.0005543508,0.000005858786,0.0006492159,0.0010413],"genre_scores_gemma":[0.8650434,0.002597823,0.1300234,0.00132462,0.00005852354,0.000406483,0.000007428199,0.00001907818,0.000519239],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8425313,"threshold_uncertainty_score":0.9999796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02348868477178454,"score_gpt":0.2541434651492976,"score_spread":0.2306547803775131,"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."}}