{"id":"W3093634451","doi":"10.1109/brains49436.2020.9223291","title":"Towards Scaling Byzantine Consensus Using Random Network Topology And Multi-Signatures","year":2020,"lang":"en","type":"article","venue":"","topic":"Distributed systems and fault tolerance","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Scalability; Adaptability; Computer science; Network topology; Distributed computing; Byzantine fault tolerance; Scale (ratio); Throughput; Topology (electrical circuits); Adaptation (eye); Convergence (economics); Computer network; Engineering; Fault tolerance; Telecommunications; Wireless","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":[0.00443326,0.0006944448,0.001021,0.0009598708,0.0008329613,0.001431546,0.001358817,0.0009750164,0.001214101],"category_scores_gemma":[0.01406943,0.0003879276,0.0005526869,0.0008335166,0.00127035,0.004922109,0.002112488,0.001252705,0.0003929701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008445699,"about_ca_system_score_gemma":0.0008238272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009807779,"about_ca_topic_score_gemma":0.0007970177,"domain_scores_codex":[0.9959379,0.002195235,0.0001409075,0.0005252594,0.0009953614,0.0002052694],"domain_scores_gemma":[0.9882359,0.005420257,0.0008875785,0.0039306,0.001231401,0.0002942344],"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":[0.0004126456,0.0003489493,0.002582509,0.0003430767,0.0001216114,0.0003172496,0.0007852014,0.7954656,0.05189674,0.04195566,0.001715861,0.1040549],"study_design_scores_gemma":[0.00004363318,0.0002836841,0.0003864031,0.00001560471,0.00002054364,0.0001348046,0.0001354389,0.9649633,0.01481209,0.0171226,0.002061705,0.00002033162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2910325,0.0003450398,0.7017071,0.0006438375,0.00007845805,0.000274862,0.00007142052,0.001770189,0.004076665],"genre_scores_gemma":[0.870774,0.0001930469,0.1274038,0.00007543613,0.00002064653,0.0001688653,0.00009287878,0.0001084179,0.001162803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00443326,"threshold_uncertainty_score":0.02344561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03984233711131498,"score_gpt":0.2785885619735802,"score_spread":0.2387462248622653,"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."}}