{"id":"W4391093845","doi":"10.1109/bigdata59044.2023.10386576","title":"RNBFT: Leveraging Randomness to Achieve Scalable Byzantine Consensus","year":2023,"lang":"en","type":"article","venue":"","topic":"Distributed systems and fault tolerance","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"University of Windsor","keywords":"Scalability; Computer science; Gossip; Byzantine fault tolerance; Distributed computing; Fault tolerance; Randomness; Overhead (engineering); Computer network; Protocol (science); Throughput; Broadcasting (networking); Telecommunications; Wireless","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":[0.002045559,0.0005006023,0.000930223,0.0005949248,0.001028074,0.0007965534,0.001554901,0.0009229422,0.00126625],"category_scores_gemma":[0.005105963,0.0002207076,0.0005114758,0.0005996667,0.001318054,0.002152633,0.002082625,0.0010252,0.00046779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005932958,"about_ca_system_score_gemma":0.0009809751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008362569,"about_ca_topic_score_gemma":0.0008046888,"domain_scores_codex":[0.9982638,0.0006013087,0.00009997126,0.000302513,0.0005792847,0.0001531793],"domain_scores_gemma":[0.9975077,0.0009309491,0.000428002,0.0006509011,0.0003258764,0.0001566832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005118098,0.000105739,0.001227101,0.0003560745,0.0001263841,0.0005931613,0.0005288824,0.6641791,0.08395246,0.1125532,0.003745054,0.132121],"study_design_scores_gemma":[0.00006964837,0.0002486955,0.0001606648,0.00001608971,0.00002032372,0.0002011151,0.00004899127,0.9388441,0.01913441,0.03428045,0.006933626,0.00004185153],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0345244,0.0002381663,0.9619168,0.0002863302,0.00008537662,0.0001122309,0.00004551197,0.000975713,0.001815402],"genre_scores_gemma":[0.7744371,0.0003255052,0.2215635,0.0002069882,0.00007963212,0.0003244502,0.0001823187,0.0001700793,0.002710248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002045559,"threshold_uncertainty_score":0.01081806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02171982947784187,"score_gpt":0.2580310264067867,"score_spread":0.2363111969289448,"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."}}