{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003072887,0.001101492,0.0003894522,0.001213888,0.001610773,0.003290089,0.001799469,0.001005799,0.004564179],"category_scores_gemma":[0.005666089,0.000521832,0.0008516966,0.001267033,0.001356688,0.005193035,0.002223645,0.00258321,0.002815432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00152344,"about_ca_system_score_gemma":0.003382235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01445667,"about_ca_topic_score_gemma":0.01561865,"domain_scores_codex":[0.9979084,0.0005328532,0.0001623665,0.0003901095,0.0007749443,0.0002314358],"domain_scores_gemma":[0.9965012,0.001071421,0.000183903,0.0007536069,0.00107875,0.0004110871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006380748,0.0005461737,0.006439428,0.0007161476,0.0001396651,0.003169588,0.01500769,0.0144563,0.1232559,0.1356023,0.07967643,0.6203523],"study_design_scores_gemma":[0.0001692484,0.0003202907,0.003681434,0.0003310539,0.0001461178,0.001566857,0.005584165,0.2036803,0.09012382,0.09147416,0.6026509,0.000271683],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06511587,0.0006013223,0.8601909,0.00674561,0.0004641003,0.0007152005,0.0004016168,0.03263143,0.03313387],"genre_scores_gemma":[0.1612732,0.0006943782,0.8172165,0.0008898782,0.000176629,0.0003104689,0.0009056054,0.003133222,0.01540012],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01445667,"threshold_uncertainty_score":0.028745,"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."}}