{"id":"W3178110043","doi":"10.48550/arxiv.2107.04947","title":"On the Performance of Pipelined HotStuff","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Byzantine fault tolerance; Latency (audio); Throughput; Protocol (science); Parallel computing; Metric (unit); Byzantine architecture; Distributed computing; Computer network; Fault tolerance; Wireless; Operating system","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":[0.003204917,0.000528394,0.0004838332,0.0009659114,0.0006875005,0.0008359613,0.0008933814,0.0007921854,0.002135316],"category_scores_gemma":[0.01249303,0.0002279157,0.0002627058,0.0008313147,0.001400136,0.002732177,0.001550492,0.0009408094,0.0002698859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001557551,"about_ca_system_score_gemma":0.001318287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003458156,"about_ca_topic_score_gemma":0.002894535,"domain_scores_codex":[0.9975268,0.000535486,0.0001507203,0.0003756385,0.0009401164,0.0004712133],"domain_scores_gemma":[0.9876648,0.006724617,0.001438616,0.002417781,0.001316624,0.0004375635],"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":[0.002928457,0.0003825661,0.01845834,0.0005064784,0.000172562,0.0004035886,0.0004714794,0.7163559,0.1205557,0.02492116,0.004913432,0.1099304],"study_design_scores_gemma":[0.00004417047,0.001002382,0.002910576,0.00002465741,0.00003727569,0.0001578169,0.0001229505,0.9487101,0.04114783,0.004887529,0.0009153046,0.00003936723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688827,0.00066653,0.0247251,0.0003943114,0.00004934915,0.00006253405,0.0002770359,0.0009812149,0.003961188],"genre_scores_gemma":[0.995451,0.0001234312,0.003676615,0.00003236777,0.000005918961,0.00002095526,0.0001527696,0.00003124502,0.0005056208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003458156,"threshold_uncertainty_score":0.01694942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03836181800933745,"score_gpt":0.1700341012809302,"score_spread":0.1316722832715927,"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."}}