{"id":"W121443288","doi":"10.1007/978-3-642-02611-9_28","title":"A New Large-Scale Multi-purpose Handwritten Farsi Database","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Spotting; Numeral system; Artificial intelligence; Speech recognition; Set (abstract data type); Natural language processing; Word (group theory); Pattern recognition (psychology); Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001201638,0.0008946216,0.0008599265,0.001481401,0.0003786017,0.001018735,0.004645755,0.0005658758,0.0001153029],"category_scores_gemma":[0.0001093431,0.0008615211,0.0002665548,0.001002597,0.00039604,0.001474989,0.002209055,0.00140395,0.000213339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003516114,"about_ca_system_score_gemma":0.001004843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005202234,"about_ca_topic_score_gemma":0.0003200951,"domain_scores_codex":[0.993942,0.0000646609,0.0008183252,0.002561525,0.001377083,0.001236366],"domain_scores_gemma":[0.9958029,0.0002708368,0.000385601,0.002604072,0.0003733908,0.0005632088],"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":[0.000005773443,0.00008078458,0.00001303074,0.00002539428,0.000008695976,0.0002539351,0.0005137734,0.0001620293,0.0003880388,0.003238598,0.0003974063,0.9949126],"study_design_scores_gemma":[0.0032748,0.001089919,0.0002657035,0.003144917,0.00006572947,0.0009917694,9.346562e-7,0.5279676,0.04022603,0.3165525,0.1015063,0.004913817],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000007351377,0.0008344925,0.9917703,0.0008040794,0.0009123886,0.0008086547,0.00003959696,0.0008768694,0.003946325],"genre_scores_gemma":[0.001965152,0.000155297,0.98943,0.004290945,0.0005569406,0.00001640052,0.00003960422,0.00005845409,0.003487204],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9899987,"threshold_uncertainty_score":0.9993836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01924858629089466,"score_gpt":0.2636876912234513,"score_spread":0.2444391049325566,"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."}}