{"id":"W1982567035","doi":"10.48550/arxiv.1105.1668","title":"Convergence Time Analysis of Quantized Gossip Consensus on Digraphs","year":2011,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ministry of Education, Culture, Sports, Science and Technology","keywords":"Convergence (economics); Gossip; Markov chain; Upper and lower bounds; Time complexity; Interval (graph theory); Markov process; Computer science; Mathematics; Lyapunov function; Algorithm; Mathematical optimization; Combinatorics","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"],"consensus_categories":[],"category_scores_codex":[0.0004707853,0.0004742497,0.001162754,0.00114067,0.0001021271,0.00007529095,0.002979342,0.0003756184,0.0001404994],"category_scores_gemma":[0.00009102164,0.0005259488,0.0009420694,0.002533583,0.0002424109,0.0001337199,0.001062251,0.0004273839,0.000333116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000145092,"about_ca_system_score_gemma":0.0002175896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000832165,"about_ca_topic_score_gemma":0.00005425362,"domain_scores_codex":[0.9968204,0.0004206135,0.0005456936,0.001523448,0.0002220626,0.0004677137],"domain_scores_gemma":[0.9954106,0.0003450848,0.0009751896,0.002635298,0.0003845806,0.0002492896],"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":[0.0005060672,0.0009312443,0.02263521,0.0002211455,0.01266027,0.001089689,0.0008448278,0.3132775,0.001730201,0.644015,0.001704624,0.0003842185],"study_design_scores_gemma":[0.0011185,0.0001004989,0.009035459,0.0001215161,0.001719173,0.000001670355,0.0000391901,0.9820264,0.0006182612,0.004355966,0.0001592988,0.0007040671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6679498,0.00009286976,0.3221441,0.00005745664,0.001029815,0.0006816212,0.0003561834,0.0004312371,0.007256927],"genre_scores_gemma":[0.9980795,0.00004012773,0.0003371634,0.00003742252,0.00001771056,0.00000161615,0.00006000528,0.00001596865,0.001410512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6687489,"threshold_uncertainty_score":0.9997192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08673103443744931,"score_gpt":0.1907673095906027,"score_spread":0.1040362751531534,"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."}}