{"id":"W4293863418","doi":"10.1109/siu55565.2022.9864678","title":"Call Intent Estimation from ATM Transactions","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Customer Service Quality and Loyalty","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Computer science; Demographics; Work (physics); Estimation; Center (category theory); Process (computing); Database transaction; Database; Operating system; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005096231,0.0007087867,0.0005202157,0.002149491,0.0002768082,0.0008912779,0.000529449,0.0007091133,0.002231881],"category_scores_gemma":[0.00274629,0.0001203525,0.0004849466,0.001192812,0.0001204696,0.0008706602,0.0005293937,0.0007940729,0.002638035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002598089,"about_ca_system_score_gemma":0.0003446501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005726173,"about_ca_topic_score_gemma":0.009097297,"domain_scores_codex":[0.9991952,0.0001304131,0.0000870221,0.0001985057,0.0002467001,0.0001421152],"domain_scores_gemma":[0.9985851,0.0004291108,0.0001766318,0.0001686348,0.0005522345,0.00008831057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001422193,0.000876674,0.4954766,0.0009589801,0.0002715622,0.0007207147,0.0007485372,0.02063009,0.01880221,0.001297543,0.04107653,0.4177184],"study_design_scores_gemma":[0.00004640983,0.0004909427,0.543696,0.0001894455,0.0001895063,0.001068271,0.001817232,0.4120561,0.01148908,0.001771983,0.02707351,0.0001116645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9062276,0.00172621,0.04456192,0.0003919763,0.0002983735,0.0002509606,0.03262539,0.00263638,0.01128117],"genre_scores_gemma":[0.9429044,0.0003053119,0.01443695,0.00009541407,0.0001027073,0.0001096548,0.0392049,0.00003885209,0.002801848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005726173,"threshold_uncertainty_score":0.01138568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04352596920783505,"score_gpt":0.2713523413911345,"score_spread":0.2278263721832995,"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."}}