{"id":"W4293863379","doi":"10.1109/siu55565.2022.9864993","title":"Banking Order Classification and Information Extraction","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Computer science; Workload; Communication source; Information extraction; Preprocessor; Database transaction; Process (computing); Support vector machine; Transaction processing; Transfer (computing); Order (exchange); Statistical classification; Artificial intelligence; Data mining; Information retrieval; Database; Operating system; Telecommunications","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":[0.0005271339,0.001002353,0.001073089,0.00655443,0.0008031693,0.00201919,0.000866525,0.0007668607,0.00732883],"category_scores_gemma":[0.002807669,0.0003703903,0.0006831302,0.003996491,0.0002496911,0.001859,0.0006482496,0.0006136315,0.009204938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006310125,"about_ca_system_score_gemma":0.001060551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003634548,"about_ca_topic_score_gemma":0.002981169,"domain_scores_codex":[0.9986577,0.0000906704,0.0002066657,0.0003227373,0.0005901192,0.0001320289],"domain_scores_gemma":[0.9980695,0.0004226601,0.000242915,0.0002852556,0.0009055268,0.00007413061],"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":[0.0003933527,0.0001908305,0.00649658,0.0002652777,0.00003456132,0.0003205516,0.0001596522,0.001908042,0.02878176,0.001263393,0.017923,0.9422629],"study_design_scores_gemma":[0.0001268204,0.0006326318,0.06332459,0.000294999,0.0003115219,0.002833517,0.0009246442,0.4042858,0.3433855,0.009192679,0.1743978,0.0002896593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1434212,0.002108099,0.7466125,0.00135215,0.0008054137,0.001638295,0.01416912,0.06009113,0.02980216],"genre_scores_gemma":[0.3592065,0.001254444,0.5909415,0.0003679584,0.00044557,0.0005430761,0.01988623,0.000796942,0.02655774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00732883,"threshold_uncertainty_score":0.02451742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0369949697109424,"score_gpt":0.2869535075704961,"score_spread":0.2499585378595537,"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."}}