{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006838086,0.0002119684,0.0001395751,0.0002058798,0.0001692485,0.0001906939,0.0002270293,0.0002479983,0.00082802],"category_scores_gemma":[0.0001628693,0.00007736358,0.0001403622,0.00005645991,0.0001893894,0.0003873819,0.0002481127,0.0001177165,0.0002107719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001365449,"about_ca_system_score_gemma":0.0001536128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007032195,"about_ca_topic_score_gemma":0.0007346481,"domain_scores_codex":[0.999948,0.000004602239,0.000002452019,0.00001014505,0.00002568798,0.000009130971],"domain_scores_gemma":[0.9999009,0.00001954447,0.00002969726,0.000008635609,0.00002903197,0.0000120837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00012798,0.00004147253,0.001859039,0.00008356009,0.000006832407,0.0001118708,0.00009569099,0.01020087,0.907998,0.0004351088,0.0002307723,0.07880895],"study_design_scores_gemma":[0.00005235763,0.001710464,0.01907921,0.00004208743,0.0000522192,0.0006638977,0.0003753488,0.2722703,0.6906972,0.00113446,0.01386447,0.00005794146],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7565309,0.0004635475,0.2336544,0.0001740856,0.00004864693,0.00004745685,0.00004265778,0.0007327155,0.008305511],"genre_scores_gemma":[0.9679304,0.0001469197,0.02936012,0.00003954575,0.000008848202,0.0000171621,0.00002488891,0.00001771049,0.002454405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00082802,"threshold_uncertainty_score":0.002770007,"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."}}