{"id":"W2073836218","doi":"10.1007/s001380050134","title":"High performance computing for industrial visual inspection","year":2000,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Visual inspection; Computer science; Artificial intelligence; Computer vision; Computer graphics (images)","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":[0.0003711508,0.000586302,0.0004903353,0.0006444971,0.0006923823,0.001420212,0.001188633,0.0006478377,0.01055317],"category_scores_gemma":[0.001716174,0.0002195454,0.0001749192,0.001966007,0.0005589582,0.00161038,0.001049322,0.001434842,0.002011605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007746185,"about_ca_system_score_gemma":0.0007831943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002364957,"about_ca_topic_score_gemma":0.002424884,"domain_scores_codex":[0.9996068,0.00007899549,0.00002311858,0.00007494894,0.0001690367,0.00004708847],"domain_scores_gemma":[0.9991962,0.0002571249,0.00004047761,0.0001950913,0.0002428576,0.00006824024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005811562,0.0001632099,0.001274436,0.0006342471,0.00005858677,0.000159516,0.0001308866,0.02796601,0.01895473,0.1164583,0.1349462,0.6986727],"study_design_scores_gemma":[0.00009783754,0.0001832118,0.002131618,0.0001890736,0.0000690643,0.0002624754,0.0001594892,0.5393101,0.02477473,0.2380441,0.1947242,0.00005422067],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05960817,0.04701081,0.7919779,0.009339926,0.003941604,0.0002479075,0.001179637,0.01119357,0.07550049],"genre_scores_gemma":[0.561336,0.01281298,0.3778275,0.001146494,0.001117512,0.0003032383,0.001596201,0.0008643936,0.04299574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01055317,"threshold_uncertainty_score":0.03530389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01216406481162484,"score_gpt":0.2566917617105863,"score_spread":0.2445276968989614,"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."}}