{"id":"W2399177260","doi":"10.1049/iet-cta.2016.0315","title":"Hierarchical nearly cyclic pursuit for consensus in large‐scale multi‐agent systems","year":2016,"lang":"en","type":"article","venue":"IET Control Theory and Applications","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Convergence (economics); Rendezvous; Computer science; Scale (ratio); Control theory (sociology); Rate of convergence; Point (geometry); Mathematical optimization; Control (management); Mathematics; Artificial intelligence; Engineering; Telecommunications; Aerospace engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.001733606,0.0002105532,0.000370181,0.000107725,0.0002287599,0.000204026,0.0007202065,0.0001262427,0.000004093558],"category_scores_gemma":[0.0001537613,0.000156939,0.0001052237,0.0002077727,0.0001357186,0.0001551474,0.00008693182,0.0001144225,0.000065366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006243249,"about_ca_system_score_gemma":0.00005694733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001159678,"about_ca_topic_score_gemma":0.00001829379,"domain_scores_codex":[0.9977394,0.0004213224,0.0005061604,0.0006216165,0.0001898155,0.0005216584],"domain_scores_gemma":[0.9969224,0.001866058,0.0001607303,0.0007300953,0.0001098505,0.0002108494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001275669,0.000300183,0.001224781,0.00003406473,0.00006329525,0.000003993793,0.0002404535,0.00006844723,0.01203618,0.966658,0.0003115921,0.01893142],"study_design_scores_gemma":[0.06087858,0.0004853576,0.02957681,0.0004485725,0.0002132776,0.0001500068,0.001010589,0.2848879,0.0006903362,0.1250471,0.494392,0.002219526],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006972992,0.0007461192,0.986108,0.00232313,0.0002618666,0.002663803,0.0005573041,0.0001773439,0.0001894506],"genre_scores_gemma":[0.9933774,0.00001364567,0.001149783,0.0002662958,0.000155798,0.004033649,0.000009972452,0.0000172265,0.0009762501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9864044,"threshold_uncertainty_score":0.6399787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01250340078394597,"score_gpt":0.2578107759632671,"score_spread":0.2453073751793211,"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."}}