{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001571806,0.0001200885,0.0002140066,0.0002261075,0.0004819379,0.0004050417,0.0009398879,0.00007801135,0.000001484343],"category_scores_gemma":[0.0004823222,0.00009954485,0.0000192125,0.0006664819,0.0002602866,0.0005095946,0.0005860022,0.0001740172,0.000003960043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001642737,"about_ca_system_score_gemma":0.0001313746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002218109,"about_ca_topic_score_gemma":3.226976e-7,"domain_scores_codex":[0.9979733,0.0003390377,0.0003348943,0.0006567808,0.0003996294,0.00029632],"domain_scores_gemma":[0.9970461,0.00105835,0.0001297613,0.001021741,0.0004565592,0.000287456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001095511,0.001280602,0.0149521,0.002533368,0.0009689461,0.0000328398,0.002428195,0.0001919655,0.1505729,0.2383138,0.5188688,0.06876098],"study_design_scores_gemma":[0.001233508,0.0006003735,0.00594529,0.00005330331,0.0000225729,0.00004872868,0.00005027961,0.9575337,0.001482703,0.0002792773,0.03255298,0.0001973339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.246648,0.007518228,0.6684213,0.05548323,0.0004260856,0.0119224,0.00307676,0.0008430887,0.005660835],"genre_scores_gemma":[0.9772484,0.0002277437,0.02140279,0.00004288066,0.00006239839,0.0001399751,0.0001788876,0.00001582427,0.0006811577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9573417,"threshold_uncertainty_score":0.4059321,"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."}}