{"id":"W2996908621","doi":"10.1109/icmla.2019.00309","title":"A Voice Interactive Multilingual Student Support System using IBM Watson","year":2019,"lang":"en","type":"preprint","venue":"","topic":"AI in Service Interactions","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Chatbot; Computer science; IBM; Personalization; Watson; World Wide Web; Dialog system; Human–computer interaction; Artificial intelligence","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004419289,0.0005575612,0.0006598014,0.0003489332,0.0001417651,0.0008432884,0.003317954,0.0003174427,0.00008831642],"category_scores_gemma":[0.00004188866,0.000521729,0.0003301453,0.0002230081,0.00002924329,0.0008575616,0.007544352,0.001613369,0.001707181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001455422,"about_ca_system_score_gemma":0.0006669174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001717877,"about_ca_topic_score_gemma":0.0001620832,"domain_scores_codex":[0.9961678,0.0002127121,0.0008372286,0.001452859,0.0007889403,0.000540464],"domain_scores_gemma":[0.9958221,0.0003418003,0.0006716125,0.002336618,0.0006550106,0.0001728733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000458398,0.006590579,0.02357445,0.01130504,0.008353723,0.002507483,0.2781386,0.5310391,0.01178703,0.02390396,0.01421436,0.08812722],"study_design_scores_gemma":[0.0004551415,0.000127114,0.001095501,0.001273937,0.0001233098,0.000266623,0.005700256,0.9809132,0.004691589,0.0001343868,0.004127254,0.001091677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.182952,0.00004211274,0.7664798,0.0004273306,0.01813034,0.00149125,0.0000319205,0.001149877,0.02929529],"genre_scores_gemma":[0.918353,0.00000348957,0.07865492,0.000360372,0.000384412,0.00006228405,0.00001970882,0.00005001584,0.002111862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7354009,"threshold_uncertainty_score":0.9997234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0410448649379407,"score_gpt":0.3660133112151571,"score_spread":0.3249684462772164,"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."}}