{"id":"W4256242627","doi":"10.32920/ryerson.14637387","title":"Implementation of Object Recognition Algorithm to enhance Manufacturing and Maintenance Tasks on an Aircraft","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Cognitive neuroscience of visual object recognition; Object (grammar); Object detection; Class (philosophy); 3D single-object recognition; Machine learning; Deep learning; Pattern recognition (psychology); Transfer of learning; Computer vision; Algorithm","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":[0.0004966971,0.0006972366,0.0003371541,0.0005766447,0.0001966887,0.0007231992,0.0009061884,0.000593571,0.002049946],"category_scores_gemma":[0.001290376,0.0002216202,0.0005465624,0.0003939406,0.0001391349,0.0007353944,0.0003431591,0.0004981889,0.001028257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005240187,"about_ca_system_score_gemma":0.000639896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004239318,"about_ca_topic_score_gemma":0.004613002,"domain_scores_codex":[0.9996892,0.00002881566,0.00002008026,0.000102165,0.0001156726,0.00004405334],"domain_scores_gemma":[0.9995596,0.0001211898,0.00005074514,0.00006879436,0.0001828213,0.00001685131],"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":[0.0003619865,0.0002600361,0.006958937,0.0003448564,0.0001112549,0.0001640387,0.00008488702,0.09992118,0.06653424,0.001349543,0.003828547,0.8200805],"study_design_scores_gemma":[0.00002417191,0.0005468254,0.0100823,0.00006885653,0.0001093277,0.0001966003,0.00007884696,0.8380964,0.1403209,0.001121687,0.009326135,0.00002808069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3854505,0.001560956,0.5916463,0.0003533768,0.0002319357,0.0002746517,0.0009018043,0.007956755,0.01162374],"genre_scores_gemma":[0.6606026,0.0008434416,0.3297729,0.000172246,0.00003248414,0.0001457804,0.001980836,0.0001507687,0.006299102],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004239318,"threshold_uncertainty_score":0.008429289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01984885456892472,"score_gpt":0.2945895539626166,"score_spread":0.2747406993936919,"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."}}