{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000232382,0.00008038249,0.0001603948,0.00004760988,0.00008280159,0.00006666034,0.0001732165,0.00007598123,0.00000556668],"category_scores_gemma":[0.000005434653,0.00006563962,0.00008272621,0.00003223441,0.00001328027,0.0001750304,0.00002376623,0.0001369262,0.000002691557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002590385,"about_ca_system_score_gemma":0.00001324999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005318526,"about_ca_topic_score_gemma":0.0000110102,"domain_scores_codex":[0.9993942,0.00002191842,0.0003053934,0.00005537751,0.00008633922,0.0001368021],"domain_scores_gemma":[0.9995517,0.00006665391,0.00006298513,0.000167674,0.00007154037,0.00007943369],"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":[0.0005239484,0.0007724413,0.04812847,0.0009063488,0.002590081,0.0001105759,0.0390837,0.1057447,0.4336116,0.004873444,0.01837707,0.3452776],"study_design_scores_gemma":[0.006236691,0.00765449,0.004751262,0.0007474186,0.0005549335,0.0006641083,0.005205801,0.6349403,0.1873536,0.01116126,0.1390817,0.001648448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4374114,0.0004094327,0.5598832,0.0008980739,0.0005334303,0.0001154537,9.932331e-7,0.0000606398,0.0006873948],"genre_scores_gemma":[0.9752364,0.00003556671,0.02410198,0.0001691805,0.0003494874,0.000001324804,6.206471e-7,0.00001761267,0.00008787666],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5378249,"threshold_uncertainty_score":0.2676706,"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."}}