{"id":"W4306918194","doi":"","title":"Pipeline following by visual servoing for Autonomous Underwater Vehicles","year":2019,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Cybernet Systems Corporation (Canada)","funders":"","keywords":"Visual servoing; Underwater; Pipeline (software); Marine engineering; Computer science; Environmental science; Aeronautics; Artificial intelligence; Geology; Engineering; Oceanography; Robot; Operating system","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":[0.000114108,0.0002414258,0.0002483354,0.000143527,0.0002909628,0.00025698,0.0003867423,0.0003658014,0.001563249],"category_scores_gemma":[0.0004139969,0.0002224293,0.000143045,0.0001468536,0.000248634,0.0003454738,0.0005151446,0.0002879957,0.0002278975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002323882,"about_ca_system_score_gemma":0.0006902694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005229316,"about_ca_topic_score_gemma":0.004022346,"domain_scores_codex":[0.9999245,0.000006942035,0.000002932387,0.00002374245,0.00002882579,0.00001299387],"domain_scores_gemma":[0.9998883,0.00003264117,0.00002360595,0.00001273647,0.00003130688,0.00001138426],"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":[0.0006974679,0.0001260316,0.001964064,0.0002829721,0.00004305422,0.0002610155,0.0003980587,0.3331507,0.3116244,0.009258295,0.002808505,0.3393854],"study_design_scores_gemma":[0.00003345023,0.0003216882,0.001612626,0.000008255103,0.00001175245,0.00006866678,0.0000278333,0.97065,0.02229727,0.002217997,0.002737183,0.0000132602],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2410118,0.0003636433,0.7500179,0.0001282679,0.00009636584,0.00006290284,0.00005966322,0.0009907791,0.007268715],"genre_scores_gemma":[0.9705668,0.00008587894,0.02344611,0.00001620461,0.000009042928,0.00002402889,0.00003458448,0.00003071912,0.00578664],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005229316,"threshold_uncertainty_score":0.01039779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007692330829188672,"score_gpt":0.2065053488126855,"score_spread":0.1988130179834968,"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."}}