{"id":"W2998142337","doi":"10.26483/ijarcs.v10i6.6497","title":"INTERACTION ANALYSIS OVER SPEECH FOR CALL CENTRE","year":2019,"lang":"en","type":"article","venue":"International Journal of Advanced Research in Computer Science","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Call centre; Speech recognition; World Wide Web; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005635104,0.0008101935,0.0005450859,0.00229077,0.0004544353,0.001132034,0.0003532875,0.0006709791,0.009341127],"category_scores_gemma":[0.002639476,0.0001271503,0.0006746449,0.00161561,0.0002935259,0.0006354689,0.0006135763,0.0005995465,0.003726873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006052915,"about_ca_system_score_gemma":0.0004529562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005076255,"about_ca_topic_score_gemma":0.004760422,"domain_scores_codex":[0.9983632,0.0002997794,0.00008475534,0.0003706151,0.0007153757,0.0001662932],"domain_scores_gemma":[0.9980612,0.001000446,0.0001309435,0.0001521574,0.0005236485,0.000131492],"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":[0.003405261,0.0006132928,0.03543932,0.001503561,0.0005829999,0.002215625,0.001853211,0.0241508,0.1926324,0.002818834,0.02836165,0.7064231],"study_design_scores_gemma":[0.0001205436,0.00136173,0.4127686,0.0001840826,0.000513288,0.003188611,0.003794372,0.4233529,0.07966327,0.006830549,0.06789495,0.0003270536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7488776,0.002563206,0.1806842,0.001128179,0.0006142028,0.0004465619,0.03409331,0.00911183,0.02248091],"genre_scores_gemma":[0.9377074,0.0005386836,0.03402653,0.0001758173,0.0002751153,0.0002871371,0.01846922,0.0004663792,0.008053774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009341127,"threshold_uncertainty_score":0.03124917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04110760722433496,"score_gpt":0.4040420514168758,"score_spread":0.3629344441925408,"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."}}