{"id":"W4387917746","doi":"10.1109/tmech.2023.3321054","title":"An Underwater Robotic System With a Soft Continuum Manipulator for Autonomous Aquatic Grasping","year":2023,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Underwater; Artificial intelligence; Computer science; Soft robotics; Adaptability; Computer vision; Robot; Controller (irrigation); Manipulator (device); Simulation; Marine engineering; Control engineering; Engineering; Geology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001443559,0.0002706886,0.000280211,0.0002121599,0.0002927229,0.000100347,0.000207062,0.0001294068,0.00001038522],"category_scores_gemma":[0.000001265749,0.0002594988,0.0001197152,0.0003956612,0.00002504102,0.000145074,0.000001224044,0.0002223702,0.000176523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002702429,"about_ca_system_score_gemma":0.00005451982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001878625,"about_ca_topic_score_gemma":0.0002250949,"domain_scores_codex":[0.9986539,0.00001718186,0.0003063181,0.0003342038,0.0001781734,0.0005101865],"domain_scores_gemma":[0.9991897,0.00009973765,0.00004253206,0.0004804366,0.00004079832,0.0001468414],"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":[0.00001066886,0.00004598968,0.000004368857,0.0001616375,0.0001050747,0.000002219799,0.00015585,0.9928722,0.002783685,0.002624508,0.00006783639,0.001166002],"study_design_scores_gemma":[0.0006065216,0.000195283,0.00002423381,0.00008522701,0.0001475433,0.00001260039,0.0005078946,0.9930993,0.004193389,0.0004000045,0.000380552,0.0003474888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02564979,0.0000252882,0.9709288,0.0001248168,0.0004908658,0.0007835816,0.00002539055,0.001932476,0.00003902141],"genre_scores_gemma":[0.9897007,0.00001220523,0.009175602,0.00002544003,0.00007194082,0.0006126896,0.00004103675,0.000140957,0.0002193672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9640509,"threshold_uncertainty_score":0.9999857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01806325064905926,"score_gpt":0.2325868427784935,"score_spread":0.2145235921294342,"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."}}