{"id":"W1746594260","doi":"10.1109/icdar.1997.620656","title":"A knowledge-based image understanding environment for document processing","year":2002,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Image processing; Knowledge-based systems; Reuse; Knowledge engineering; Artificial intelligence; Knowledge acquisition; Inference; Context (archaeology); Cognitive neuroscience of visual object recognition; Problem statement; Inference engine; Image (mathematics); Object (grammar); 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":[0.0001668008,0.00009163485,0.0000791844,0.00006588919,0.0001601707,0.000211319,0.0003124407,0.00003026886,0.0001315807],"category_scores_gemma":[0.000009647913,0.00007596856,0.0000528379,0.0001393783,0.00004176222,0.0003527759,0.00004964374,0.00003962902,0.00007270767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000220336,"about_ca_system_score_gemma":0.00001811367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.599947e-7,"about_ca_topic_score_gemma":2.31545e-7,"domain_scores_codex":[0.9992604,0.00001668752,0.0001486064,0.0002561805,0.0001322735,0.0001858388],"domain_scores_gemma":[0.9995858,0.00004600473,0.00005373625,0.0002350149,0.00002576388,0.00005365365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001111709,0.0005884916,0.00002685743,0.0002307676,0.00001672951,0.000003995191,0.00106949,0.000005343813,0.02512911,0.325006,0.008693977,0.6392181],"study_design_scores_gemma":[0.0007065455,0.0001922786,0.00002517844,0.00004514415,0.000009791556,0.000003209038,0.0001033111,0.6576551,0.2498798,0.02214683,0.06885977,0.0003729872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000006905103,0.0001857207,0.9883637,0.002324209,0.00002926931,0.0002656335,4.854373e-7,0.0003095366,0.008514553],"genre_scores_gemma":[0.4879107,0.00002341095,0.5079972,0.0002637927,0.00002986785,0.0001122422,0.000001266614,0.00001013214,0.003651312],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6576498,"threshold_uncertainty_score":0.3097908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07431386826400342,"score_gpt":0.282189523563462,"score_spread":0.2078756552994586,"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."}}