{"id":"W2883175617","doi":"10.1007/s10845-018-1438-3","title":"A flexible machine vision system for small part inspection based on a hybrid SVM/ANN approach","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Support vector machine; Artificial intelligence; Machine learning; Robustness (evolution); Machine vision; Artificial neural network; Computer science; Pattern recognition (psychology); Flexibility (engineering); Feature extraction; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009165176,0.0002413724,0.0003750959,0.0005093968,0.0001868798,0.0001195926,0.0001629737,0.0001254914,0.00002316992],"category_scores_gemma":[0.00004747306,0.0001918077,0.0002919934,0.0001139605,0.00002021359,0.0001451047,0.00001588006,0.0003564169,0.00003713784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004271164,"about_ca_system_score_gemma":0.00002485144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001513697,"about_ca_topic_score_gemma":0.000002956416,"domain_scores_codex":[0.9983383,0.00005668472,0.0008032725,0.0001892263,0.0003324675,0.0002800863],"domain_scores_gemma":[0.9990632,0.00009864898,0.0003085842,0.0002236839,0.0001724865,0.000133392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003439316,0.0004365851,0.00008037052,0.001876591,0.0005009652,0.00006142753,0.0007209463,0.7443256,0.01077462,0.0001043849,0.01416445,0.2235147],"study_design_scores_gemma":[0.000701965,0.001027677,0.00003232231,0.000552431,0.00003837522,0.000164494,0.0001717281,0.1763986,0.7892901,0.0000158922,0.0314099,0.0001964135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2920703,0.0001050483,0.6990655,0.00001810816,0.004851125,0.0004734489,0.00001082082,0.0002879077,0.00311773],"genre_scores_gemma":[0.9961135,0.00001041155,0.001282708,0.00002539788,0.002395386,0.0000153219,0.000004419824,0.00005350634,0.00009933471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7785155,"threshold_uncertainty_score":0.7821691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02892176681014924,"score_gpt":0.2454527085108482,"score_spread":0.2165309417006989,"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."}}