{"id":"W2011306156","doi":"10.1016/j.rcim.2012.07.004","title":"Remote robotic underwater grinding system and modeling for rectification of hydroelectric structures","year":2012,"lang":"en","type":"article","venue":"Robotics and Computer-Integrated Manufacturing","topic":"Advanced Surface Polishing Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydro-Québec; École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Grinding; Underwater; Drag; Power (physics); Marine engineering; Process (computing); Mechanical engineering; Hydroelectricity; Automotive engineering; Engineering; Computer science; Geology; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001588589,0.0002249706,0.0002930833,0.0001710102,0.0000999139,0.00006827233,0.00009867294,0.0001149286,3.778435e-7],"category_scores_gemma":[0.00000660934,0.0002063112,0.0000385266,0.00007489045,0.00001899738,0.0002271729,0.00004394235,0.0001892434,2.3297e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001150725,"about_ca_system_score_gemma":0.000005450765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004870855,"about_ca_topic_score_gemma":0.00000423136,"domain_scores_codex":[0.9990438,0.00002094887,0.0002995752,0.0001931873,0.00008860928,0.0003539263],"domain_scores_gemma":[0.9995524,0.00007728108,0.00006720902,0.0001723324,0.00003897767,0.0000917657],"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":[0.000006309617,0.000004336503,0.00007748849,0.00040866,0.00004352839,3.835637e-7,0.0001727673,0.9779886,0.002783563,0.001814574,0.000009060962,0.01669074],"study_design_scores_gemma":[0.0001311264,0.0000289183,0.0001685453,0.0001562244,0.00003687381,0.00002721841,0.00006579017,0.9214095,0.0765243,0.001233065,0.00001690183,0.0002014627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3258656,0.0003513955,0.6730322,0.000008000413,0.0001997657,0.0001958207,0.000002392505,0.0003240877,0.00002074851],"genre_scores_gemma":[0.7464601,0.00007340704,0.2533357,0.000003977061,0.00007127414,0.000003324627,0.000009307392,0.00003882008,0.000004099594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4205944,"threshold_uncertainty_score":0.8413125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02106171130944982,"score_gpt":0.2324379325393029,"score_spread":0.2113762212298531,"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."}}