{"id":"W4290669654","doi":"10.1007/11867661","title":"Image Analysis and Recognition","year":2006,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Computer vision","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":[0.0003431639,0.00133965,0.001100993,0.001828671,0.0004508613,0.001739363,0.001564861,0.0009592464,0.08801275],"category_scores_gemma":[0.0006157664,0.0005656052,0.000691701,0.001667856,0.0005443749,0.001291226,0.001156115,0.0008940515,0.11448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003188538,"about_ca_system_score_gemma":0.0005161208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008470313,"about_ca_topic_score_gemma":0.001334625,"domain_scores_codex":[0.9995945,0.00001859993,0.00001937166,0.0001046192,0.0002354965,0.00002734454],"domain_scores_gemma":[0.9996305,0.00004558841,0.00001320776,0.0001362514,0.0001539635,0.00002046472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004224272,0.00003364028,0.00007826979,0.0002727199,0.00002489064,0.00005711135,0.00002899459,0.000886152,0.03383862,0.008814855,0.1078564,0.8480662],"study_design_scores_gemma":[0.00001276502,0.00008783837,0.001009372,0.0001021198,0.00004664087,0.0008725844,0.00003901743,0.01183233,0.06685869,0.0119543,0.9071428,0.0000415001],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.001996015,0.01023912,0.8288749,0.0005349094,0.001597798,0.0002549678,0.00150668,0.01350381,0.1414917],"genre_scores_gemma":[0.01938079,0.01138534,0.336359,0.0007050806,0.0006414578,0.0003019164,0.005054634,0.001891163,0.6242807],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.08801275,"threshold_uncertainty_score":0.294432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01186675700244924,"score_gpt":0.2204167698162916,"score_spread":0.2085500128138424,"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."}}