{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002102424,0.0001322755,0.0002831917,0.00001732691,0.0001383503,0.0001247714,0.0002805107,0.00008805775,0.00001143531],"category_scores_gemma":[0.00006773907,0.0001074774,0.00004544446,0.0002522164,0.0000679843,0.00008174164,0.000193658,0.0001332959,0.000005348712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004506765,"about_ca_system_score_gemma":0.00003980139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001221963,"about_ca_topic_score_gemma":0.000008166616,"domain_scores_codex":[0.9988924,0.00008308885,0.0002503484,0.0003616375,0.0001187016,0.0002938452],"domain_scores_gemma":[0.9994662,0.00007655124,0.00007433797,0.0001752196,0.00006200483,0.0001457123],"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":[0.0007239992,0.0003062273,0.01176509,0.0005019752,0.0005734405,0.00119069,0.007351973,0.1458728,0.02998534,0.5297841,0.0496437,0.2223006],"study_design_scores_gemma":[0.001689948,0.00002401056,0.0005002987,0.00002217171,0.000006256109,0.00003817963,0.00004356063,0.9899076,0.000331137,0.0001076153,0.007164342,0.0001648906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02270688,0.001824849,0.9711284,0.002745726,0.0004080497,0.0001520266,0.000005163612,0.000162909,0.0008659592],"genre_scores_gemma":[0.8483838,0.00001179214,0.149669,0.001681177,0.0002071091,0.000001818286,0.00000162858,0.000005846525,0.00003790633],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8440348,"threshold_uncertainty_score":0.43828,"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."}}