{"id":"W2444607264","doi":"10.1109/oceansap.2016.7485504","title":"Optimal design of consensus for autonomous underwater vehicles with damping term using a directed spanning tree","year":2016,"lang":"en","type":"article","venue":"OCEANS 2016 - Shanghai","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Spanning tree; Term (time); Control theory (sociology); Heading (navigation); Lyapunov function; Computer science; Underwater; Tree (set theory); Consensus; Minimum spanning tree; Function (biology); Multi-agent system; Topology (electrical circuits); Network topology; Mathematical optimization; Mathematics; Control (management); Engineering; Algorithm; Artificial intelligence; Physics","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":[0.0004615538,0.0003027862,0.0004465666,0.0001870977,0.0001579492,0.0001303652,0.0007110224,0.0001114679,0.000007819163],"category_scores_gemma":[0.00007304538,0.0001959887,0.00010807,0.0002358691,0.0001388252,0.000359048,0.0001351672,0.00006255769,0.00001312066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001777811,"about_ca_system_score_gemma":0.0002753009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000437033,"about_ca_topic_score_gemma":0.000007811093,"domain_scores_codex":[0.997857,0.0001452612,0.0004706159,0.0006004801,0.00028604,0.0006405679],"domain_scores_gemma":[0.9981027,0.0005178067,0.0003571235,0.0006425056,0.0002398476,0.0001400812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008752618,0.0004307842,0.005561859,0.0002530694,0.0009403817,0.0001754113,0.00269788,0.01992088,0.9113792,0.002685313,0.004019789,0.05106015],"study_design_scores_gemma":[0.007176209,0.000621395,0.002114935,0.001519775,0.0001421837,0.0001271186,0.0001171247,0.9363089,0.04961935,0.0002091643,0.001085471,0.0009583456],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09853426,0.0001038741,0.8995799,0.0004004401,0.0001838353,0.0007103175,0.00007417279,0.0003273329,0.00008580756],"genre_scores_gemma":[0.851773,0.000002452379,0.147505,0.00004050729,0.00009075132,0.00003296198,0.000005307744,0.00004055172,0.0005094481],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.916388,"threshold_uncertainty_score":0.7992187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0419737512940576,"score_gpt":0.2580276590049715,"score_spread":0.2160539077109139,"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."}}