{"id":"W3099972933","doi":"10.3929/ethz-b-000461784","title":"Performance and Design of Consensus on Matrix-Weighted and Time-Scaled Graphs","year":2020,"lang":"en","type":"article","venue":"Repository for Publications and Research Data (ETH Zurich)","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Office; Army Research Laboratory; Air Force Office of Scientific Research; National Science Foundation of Sri Lanka; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Consensus; Uniform consensus; Consensus algorithm; Matrix (chemical analysis); Graph theory; Mathematical optimization; Mathematics; Algorithm; Combinatorics; Artificial intelligence; Multi-agent system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00311571,0.001018818,0.001160498,0.000691354,0.0006214037,0.001296778,0.00130843,0.001308006,0.002148256],"category_scores_gemma":[0.01009141,0.0004343849,0.0005156816,0.00062547,0.001050333,0.001261309,0.001283231,0.0007296806,0.0003122283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001711395,"about_ca_system_score_gemma":0.001832137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005206868,"about_ca_topic_score_gemma":0.003284751,"domain_scores_codex":[0.9986487,0.0005463671,0.00005416793,0.0003194941,0.0002467505,0.0001844854],"domain_scores_gemma":[0.9931531,0.004581553,0.0005250567,0.0003013233,0.00114217,0.0002967762],"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":[0.0001078797,0.000034153,0.0001796214,0.00004926341,0.00001818725,0.0000259855,0.00003963416,0.9841391,0.001949526,0.00541804,0.0002800978,0.007758524],"study_design_scores_gemma":[0.0000104232,0.00005460952,0.00004683271,0.000002367212,0.000004224969,0.000005115387,0.000008693339,0.9973111,0.0003397507,0.002167619,0.00004661259,0.000002642816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1732214,0.0001972839,0.8199357,0.0003906229,0.00007657376,0.0001576442,0.00009907057,0.0003711412,0.005550478],"genre_scores_gemma":[0.9700145,0.00008187884,0.02794078,0.00003974088,0.00001818379,0.00007166338,0.00007165026,0.00003398118,0.001727687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005206868,"threshold_uncertainty_score":0.01647764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1252368486719832,"score_gpt":0.342692922188972,"score_spread":0.2174560735169888,"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."}}