{"id":"W2782779765","doi":"","title":"Using IBM watson cloud services to build natural language processing solutions to leverage chat tools","year":2017,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada)","funders":"","keywords":"IBM; Watson; Computer science; Leverage (statistics); Cloud computing; World Wide Web; Cognitive computing; Multimedia; Data science; Artificial intelligence; Cognition; Operating system","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007114661,0.0001999998,0.0002044011,0.0002645878,0.001211292,0.003036265,0.001926445,0.00004551967,5.70977e-7],"category_scores_gemma":[0.000235848,0.0001864741,0.00003238016,0.0005341269,0.00005572621,0.002494337,0.001508714,0.0001303764,0.00001435072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001029281,"about_ca_system_score_gemma":0.0001331734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001105829,"about_ca_topic_score_gemma":0.00001897999,"domain_scores_codex":[0.9980628,0.00001148704,0.0001832662,0.0006197382,0.0004663233,0.0006564003],"domain_scores_gemma":[0.9985374,0.00004869217,0.00006649928,0.00077928,0.0002058223,0.0003623388],"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":[0.000007480985,0.00003700335,0.002175169,0.0003267872,0.00001569612,0.000115236,0.01602316,0.03794833,0.03335243,0.001391101,0.0001796707,0.908428],"study_design_scores_gemma":[0.0002042202,0.00006219093,0.01679966,0.0004450787,0.000004373971,0.00008816146,0.00005108142,0.9771942,0.002957608,0.00002831621,0.001636877,0.000528201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2106897,0.0006066493,0.786063,0.0002438184,0.001943478,0.0001833173,0.000001982237,0.0002582173,0.000009821737],"genre_scores_gemma":[0.6903285,0.000001883593,0.3089399,0.0002881629,0.0004131412,0.000006835111,5.793788e-7,0.000008617507,0.00001237149],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9392459,"threshold_uncertainty_score":0.9979987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02687267921664701,"score_gpt":0.265047471642919,"score_spread":0.238174792426272,"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."}}