{"id":"W3185664833","doi":"10.11591/ijra.v10i3.pp207-223","title":"Increasing the operating depth of an autonomous underwater vehicle using an intelligent magnetic field","year":2021,"lang":"en","type":"article","venue":"IAES International Journal of Robotics and Automation (IJRA)","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Underwater; Robot; Power (physics); Drag; Electromagnetic coil; Computer science; Automotive engineering; Simulation; Marine engineering; Engineering; Electrical engineering; Aerospace engineering; Artificial intelligence","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":[0.000354712,0.00008957751,0.0001419511,0.00008225302,0.00007605371,0.0002249107,0.0002829152,0.00006011021,0.00002730737],"category_scores_gemma":[0.00001736531,0.00007069616,0.00004923095,0.00006653497,0.00002725781,0.000351716,0.00005400626,0.0001753762,7.959973e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005336434,"about_ca_system_score_gemma":0.00004631081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004230495,"about_ca_topic_score_gemma":0.00003329241,"domain_scores_codex":[0.9989413,0.0001191084,0.00052245,0.00007196673,0.0002565652,0.00008859133],"domain_scores_gemma":[0.999176,0.00008862498,0.000167746,0.0001485189,0.0003646433,0.00005446513],"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":[0.00001322342,0.0001187906,0.002292849,0.00003703598,0.000154414,0.00002137386,0.002733055,0.7616679,0.1433983,0.00101938,0.000009128791,0.08853459],"study_design_scores_gemma":[0.0002676224,0.0001153169,0.002723944,0.000144427,0.00002875906,0.0005393219,0.001462671,0.9387807,0.05500567,0.0005103964,0.0003128628,0.0001082895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8861315,0.0004834376,0.1124433,0.0004979214,0.0002342553,0.0000408302,0.000001982616,0.00002147712,0.0001453021],"genre_scores_gemma":[0.9705804,0.0000936322,0.02904968,0.0001213892,0.0001241079,6.719757e-7,0.000005141415,0.00001380862,0.00001118317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1771128,"threshold_uncertainty_score":0.2882906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02713229882077106,"score_gpt":0.2734558082354536,"score_spread":0.2463235094146826,"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."}}