{"id":"W2009674573","doi":"10.1155/2013/832963","title":"Rapid 3D Modeling and Parts Recognition on Automotive Vehicles Using a Network of RGB-D Sensors for Robot Guidance","year":2013,"lang":"en","type":"article","venue":"Journal of Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automotive industry; Profiling (computer programming); RGB color model; Artificial intelligence; Robot; Computer vision; Computer science; 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":[0.000266123,0.0001484466,0.0003051962,0.0001069977,0.0000657803,0.00003510796,0.00004712601,0.00009453103,0.0000109502],"category_scores_gemma":[0.0001115338,0.0001366804,0.00008937209,0.0001226031,0.00002767417,0.0001797159,0.000006517846,0.0001430866,0.000002005597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007251943,"about_ca_system_score_gemma":0.00001886699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001122081,"about_ca_topic_score_gemma":0.000001038485,"domain_scores_codex":[0.9989002,0.00004953202,0.0005554649,0.0001033804,0.0001764135,0.0002150432],"domain_scores_gemma":[0.9989514,0.0001439901,0.0002314558,0.00008476248,0.0005010443,0.00008732647],"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":[0.00003787699,0.00001425084,0.00005436991,0.00006935203,0.00005948324,0.000003303192,0.0001907266,0.988559,0.006940909,0.00001305903,0.000212752,0.003844901],"study_design_scores_gemma":[0.0004739487,0.0001673024,0.0002078221,0.0004201873,0.00005599888,0.00002955243,0.0001491284,0.9922166,0.005655959,0.0004452422,0.00003574029,0.0001425038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9168578,0.0003294276,0.08220959,0.00007248353,0.0002603289,0.0001916804,0.000005392305,0.00002277996,0.00005052264],"genre_scores_gemma":[0.9597709,0.0002660917,0.03957284,0.00004419071,0.0002976757,0.000001576641,0.000002699998,0.00003750897,0.000006490298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04291313,"threshold_uncertainty_score":0.5573666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03720521182086696,"score_gpt":0.2342505789125666,"score_spread":0.1970453670916997,"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."}}