{"id":"W2772804860","doi":"10.1109/tsmc.2017.2774841","title":"Distributed Consensus of Linear Multiagent Systems: Laplacian Spectra-Based Method","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Network topology; Laplace operator; Norm (philosophy); Stability (learning theory); Multi-agent system; Frequency domain; Computer science; Function (biology); Domain (mathematical analysis); Linear system; Laplacian matrix; Mathematical optimization; Topology (electrical circuits); Mathematics; Applied mathematics; Artificial intelligence; Combinatorics; Machine learning; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001641063,0.0007210882,0.001430652,0.0003860413,0.0009538678,0.001305297,0.00174655,0.0004320937,0.000003881042],"category_scores_gemma":[0.00005087765,0.0006801453,0.000328594,0.0002998108,0.0002631095,0.0002625065,0.00001960079,0.0004563652,0.00008019882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002652428,"about_ca_system_score_gemma":0.0001962254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004701694,"about_ca_topic_score_gemma":0.0001185666,"domain_scores_codex":[0.9941064,0.001012395,0.001751445,0.001170428,0.001130436,0.0008289276],"domain_scores_gemma":[0.9937711,0.0005533955,0.001561353,0.003038071,0.0005404862,0.0005356165],"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":[0.0004391762,0.002010507,0.001218499,0.005803192,0.002314057,0.0005737758,0.001787698,0.9255905,0.01307175,0.03944929,0.002764067,0.004977471],"study_design_scores_gemma":[0.003498812,0.000376333,0.0004186713,0.001228975,0.0001812513,0.0002268839,0.0007451907,0.9827957,0.003288651,0.000008728332,0.006425315,0.0008055004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006111271,0.001074185,0.9808074,0.0002097482,0.006821121,0.002351972,0.001261422,0.0003619227,0.001000951],"genre_scores_gemma":[0.9963371,0.0000326744,0.001806811,0.00002011642,0.0002026484,0.0003044386,0.00002061698,0.00006822945,0.001207382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9902258,"threshold_uncertainty_score":0.9997314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02757853974605842,"score_gpt":0.2769596681704634,"score_spread":0.249381128424405,"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."}}