{"id":"W2791545383","doi":"10.1002/rob.21776","title":"Teach‐and‐repeat path following for an autonomous underwater vehicle","year":2018,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Australian Research Council; Atlantic Canada Opportunities Agency; Research and Development Corporation of Newfoundland and Labrador; Australian Government","keywords":"Sonar; Offset (computer science); Path (computing); Computer science; Underwater; Real-time computing; Motion planning; Computer vision; Artificial intelligence; Process (computing); Engineering; Geography; Robot","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0001649542,0.0003076029,0.0002656073,0.0002122683,0.0004700058,0.0001998146,0.000754962,0.0003746951,0.001908518],"category_scores_gemma":[0.0005093929,0.0001737403,0.0001629567,0.0000980332,0.0002729217,0.0002880215,0.0005225552,0.0003363539,0.000307997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002863379,"about_ca_system_score_gemma":0.0006529808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006721786,"about_ca_topic_score_gemma":0.007466242,"domain_scores_codex":[0.9998839,0.00001595345,0.000003618872,0.00003213074,0.00004966133,0.00001474387],"domain_scores_gemma":[0.9997813,0.00006077834,0.00003381776,0.00003729878,0.00006022777,0.00002657385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000266952,0.0002788966,0.004259854,0.0001083495,0.00007694781,0.0004102979,0.0006877144,0.2347402,0.1740718,0.004955406,0.002290457,0.5778531],"study_design_scores_gemma":[0.00003357797,0.0002721467,0.00102337,0.000006261837,0.00001728864,0.0001503396,0.00007007286,0.9645354,0.0287807,0.0009892127,0.004099099,0.000022472],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2343363,0.00007135333,0.7576555,0.0001053717,0.0000283975,0.00009183714,0.00003722305,0.002986023,0.004687968],"genre_scores_gemma":[0.7605087,0.00002678791,0.2339342,0.00003604673,0.000006901444,0.00005940552,0.00004936882,0.00006836744,0.005310337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006721786,"threshold_uncertainty_score":0.01336533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02452911160941314,"score_gpt":0.2594595319908953,"score_spread":0.2349304203814822,"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."}}