{"id":"W2530293644","doi":"10.2196/publichealth.5810","title":"IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics","year":2016,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Analytics; IBM; Data science; Computer science; Predictive analytics; Watson; Visual analytics; Software analytics; Data visualization; Descriptive statistics; Business analytics; Exploratory data analysis; Visualization; Software; Data mining; Statistics; Software development; Artificial intelligence; Software development process","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.01391013,0.004146384,0.001992506,0.01051154,0.001284335,0.009575996,0.004135284,0.001477482,0.02260266],"category_scores_gemma":[0.05118933,0.002058336,0.002138745,0.01156382,0.001532273,0.007098948,0.005556087,0.004033142,0.02275022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001244761,"about_ca_system_score_gemma":0.006232047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006423134,"about_ca_topic_score_gemma":0.004497255,"domain_scores_codex":[0.9849561,0.003557432,0.001695418,0.001874232,0.007475584,0.0004412128],"domain_scores_gemma":[0.9706336,0.01486915,0.002149724,0.0042433,0.0072357,0.0008684624],"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":[0.0006361362,0.000250939,0.005147254,0.002243158,0.0003715716,0.0006758705,0.00152097,0.011092,0.007014088,0.03880937,0.4554049,0.4768339],"study_design_scores_gemma":[0.0004471601,0.0002321762,0.006265242,0.001521391,0.0002269093,0.0008932712,0.0007238226,0.1667419,0.02148116,0.1779828,0.6230071,0.0004769766],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.004438286,0.002220485,0.7528282,0.002931933,0.0005143064,0.001404942,0.02204946,0.1902128,0.0233995],"genre_scores_gemma":[0.02862318,0.002496971,0.9230679,0.0006909394,0.0004305718,0.001876285,0.02464778,0.01311505,0.005051263],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02260266,"threshold_uncertainty_score":0.07561338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1673281824402659,"score_gpt":0.4277183124770265,"score_spread":0.2603901300367606,"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."}}