{"id":"W2043466552","doi":"10.1109/ais.2010.5547032","title":"A software framework for multi-agent control of multiple autonomous underwater vehicles for underwater mine counter-measures","year":2010,"lang":"en","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; University of New Brunswick","funders":"","keywords":"Underwater; Sonar; Seabed; Remotely operated underwater vehicle; Controller (irrigation); Computer science; Marine engineering; Real-time computing; Robot; Remotely operated vehicle; Software; Mobile robot; Simulation; Engineering; Artificial intelligence; Geology; Oceanography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003085457,0.0002746327,0.0004161392,0.00009273735,0.0001179881,0.00007862764,0.0004219216,0.0002636334,0.00004174365],"category_scores_gemma":[0.00002988955,0.0002238149,0.0002304443,0.000060673,0.00007310849,0.00008964184,0.0000414093,0.0002062506,0.00001673501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004966372,"about_ca_system_score_gemma":0.00002659886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000849552,"about_ca_topic_score_gemma":0.0006369058,"domain_scores_codex":[0.9985655,0.00002701833,0.000583196,0.0002533379,0.0001582227,0.0004127417],"domain_scores_gemma":[0.9984934,0.0004757522,0.00009152821,0.0005908596,0.0002374572,0.0001109858],"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":[0.0004287972,0.0007183817,0.02069796,0.001567947,0.001240021,0.000001580315,0.003291231,0.01704042,0.9219289,0.003283122,0.001312526,0.02848914],"study_design_scores_gemma":[0.007701091,0.0002569942,0.001578996,0.000148088,0.0001360276,0.00001202989,0.0006573508,0.4874921,0.3728408,0.0042482,0.1239995,0.0009288409],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1264865,0.0001446627,0.8712895,0.0002161934,0.0002259072,0.001117062,0.0001604232,0.0003369106,0.00002284571],"genre_scores_gemma":[0.770627,0.000006345191,0.2282744,0.0001387186,0.00009539869,0.0004508104,0.00003098914,0.00007433355,0.0003019671],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6441405,"threshold_uncertainty_score":0.9126906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03719696667570542,"score_gpt":0.2616707602385464,"score_spread":0.2244737935628409,"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."}}