{"id":"W2109073894","doi":"10.1109/robot.2002.1013616","title":"Automated inspection system using range data","year":2003,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Point cloud; Computer science; Range (aeronautics); Process (computing); Noise (video); Set (abstract data type); Artificial intelligence; Dispersion (optics); Computer vision; Automated X-ray inspection; CAD; Engineering drawing; Engineering; Image processing; Optics","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":[0.0006299113,0.0005644081,0.0008833451,0.001359582,0.0003569815,0.00067872,0.001070296,0.0008634169,0.002468791],"category_scores_gemma":[0.002106069,0.0004271991,0.0004290111,0.0006577489,0.0003955022,0.001071284,0.000857336,0.0003720258,0.00134459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002646087,"about_ca_system_score_gemma":0.0003334424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001161828,"about_ca_topic_score_gemma":0.0007582837,"domain_scores_codex":[0.9988638,0.0002261328,0.00004812018,0.000273223,0.0005191724,0.00006955273],"domain_scores_gemma":[0.99861,0.0004338736,0.0001594995,0.0003786013,0.0003750053,0.00004291684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005929553,0.0002069733,0.004101291,0.000220628,0.0001031236,0.0004751305,0.0002792058,0.04342248,0.2029865,0.003713409,0.004979736,0.7389185],"study_design_scores_gemma":[0.0002128035,0.0008548453,0.01058956,0.00009543517,0.0001458336,0.001757185,0.0001317983,0.8181164,0.1449407,0.005086722,0.01790828,0.0001603951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06114254,0.0003907161,0.9247545,0.0001077304,0.00005250041,0.0001007829,0.0001287945,0.01072436,0.00259818],"genre_scores_gemma":[0.6407956,0.0002162328,0.3549025,0.0001242308,0.00008190664,0.0001546682,0.0003923196,0.000177765,0.003154634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002468791,"threshold_uncertainty_score":0.008258939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07293630210380629,"score_gpt":0.2725051493526376,"score_spread":0.1995688472488313,"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."}}