{"id":"W1981726670","doi":"10.1109/i2mtc.2012.6229245","title":"Dual supervisory architecture for drift correction and accurate visual servoing in industrial manufacturing","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Visual servoing; Artificial intelligence; Computer vision; Computer science; Feature (linguistics); Automotive industry; Tracking (education); Feature extraction; Estimator; Matching (statistics); Stereopsis; Factory (object-oriented programming); Field (mathematics); Robot; Engineering; Mathematics","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":[0.0003241477,0.0002593785,0.0003274831,0.0002286631,0.0002768747,0.0004119209,0.0008527267,0.0003042218,0.001353438],"category_scores_gemma":[0.000604457,0.0001843912,0.0001404702,0.0001557076,0.0002991865,0.0003353399,0.0004893664,0.0004604353,0.0003984212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002910835,"about_ca_system_score_gemma":0.0005705362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008791495,"about_ca_topic_score_gemma":0.001197035,"domain_scores_codex":[0.9997402,0.00003320663,0.00001485301,0.00007726216,0.000105389,0.00002907187],"domain_scores_gemma":[0.9996796,0.00006383942,0.0000539936,0.00007674477,0.0001056196,0.00002031316],"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":[0.0006819274,0.000337993,0.002072591,0.0002269047,0.0000396252,0.0002103829,0.0003996141,0.1159777,0.2494209,0.006528638,0.00248031,0.6216235],"study_design_scores_gemma":[0.00008371109,0.0004914463,0.001717771,0.00001752989,0.00002190561,0.0001585569,0.00003436666,0.9255632,0.06365664,0.003715568,0.004518194,0.00002104277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08293334,0.0002615915,0.9111822,0.00006971419,0.00007670486,0.00004235515,0.00002616295,0.002911241,0.002496626],"genre_scores_gemma":[0.9373622,0.00006670464,0.06061934,0.00003191146,0.00003424353,0.00005795234,0.00003698501,0.00002719255,0.001763572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001353438,"threshold_uncertainty_score":0.004527688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0461480973143737,"score_gpt":0.2957944672117913,"score_spread":0.2496463698974176,"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."}}