{"id":"W2460796372","doi":"","title":"Image Analysis and Recognition: 8th International Conference, ICIAR 2011, Burnaby, BC, Canada, June 22-24, 2011. Proceedings, Part I (Lecture Notes in ... Vision, Pattern Recognition, and Graphics)","year":2011,"lang":"en","type":"book","venue":"Springer eBooks","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Biometrics; Artificial intelligence; Coding (social sciences); Computer vision; Graphics; Image processing; Facial recognition system; Feature (linguistics); Feature extraction; Pattern recognition (psychology); Image (mathematics); Computer graphics (images); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004143055,0.002985547,0.002833259,0.003850561,0.001092101,0.00379882,0.002779779,0.002175209,0.0355591],"category_scores_gemma":[0.002318428,0.001007115,0.0009758027,0.00329973,0.001908578,0.002630714,0.002308478,0.003325847,0.04058819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00205418,"about_ca_system_score_gemma":0.003744581,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03183011,"about_ca_topic_score_gemma":0.05490326,"domain_scores_codex":[0.9982612,0.000183015,0.0001111267,0.0002907191,0.0009480377,0.0002058719],"domain_scores_gemma":[0.9958922,0.0002676451,0.00007685836,0.0003382048,0.00302525,0.0003999949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001782269,0.0001054537,0.000285135,0.0003866038,0.00005211302,0.00009196886,0.00005834629,0.0007996896,0.005290573,0.0008864843,0.6975317,0.2943338],"study_design_scores_gemma":[0.00005120924,0.0002472799,0.005808559,0.0004674886,0.0001518674,0.001280095,0.0002274887,0.01409152,0.01315029,0.002372955,0.9620433,0.0001078342],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01533007,0.3218497,0.4608639,0.01025017,0.03933774,0.000988342,0.009102836,0.03091461,0.1113627],"genre_scores_gemma":[0.02754914,0.1432781,0.2068873,0.002382584,0.004226807,0.0005871951,0.02386492,0.004981667,0.5862423],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9681699,"threshold_uncertainty_score":0.118957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02394896213459699,"score_gpt":0.210998171829651,"score_spread":0.187049209695054,"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."}}