{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004132644,0.0005481856,0.0007407376,0.001077362,0.0001218955,0.0002274996,0.0001694894,0.0006774209,0.001520058],"category_scores_gemma":[0.00003827395,0.0005562149,0.000146737,0.0001129968,0.00007865034,0.0001700308,0.00009176697,0.0009112003,0.00007314364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002139598,"about_ca_system_score_gemma":0.0001983216,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04896026,"about_ca_topic_score_gemma":0.1726657,"domain_scores_codex":[0.9977075,0.00003484858,0.0007857388,0.0006587545,0.0004392602,0.0003738461],"domain_scores_gemma":[0.9987004,0.00007576091,0.0003013283,0.0002101865,0.0005148616,0.00019748],"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":[0.0005550217,0.0001686126,0.01263138,0.002778301,0.009522079,0.0005780772,0.003514542,0.00003213412,0.003166841,0.0002311142,0.2417804,0.7250416],"study_design_scores_gemma":[0.01034444,0.0009334022,0.02088855,0.01096666,0.007482067,0.0006021713,0.0005334888,0.0115831,0.01933054,0.05987759,0.8440681,0.0133899],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1749001,0.003469552,0.0137769,0.00048399,0.01985375,0.005541776,0.004685119,0.001439741,0.7758491],"genre_scores_gemma":[0.8781725,0.004203881,0.001540736,0.0006502358,0.007971927,0.0007573254,0.002805854,0.0008549644,0.1030426],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7116516,"threshold_uncertainty_score":0.9996889,"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."}}