{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001756441,0.000157097,0.0003291546,0.0001226994,0.0002219092,0.00008751483,0.00007715652,0.00006938807,0.00005254736],"category_scores_gemma":[0.009939719,0.0001112248,0.00001429446,0.0001854901,0.000163461,0.000130318,0.00005072938,0.00007542044,0.0000042209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001014385,"about_ca_system_score_gemma":0.0004292605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001239895,"about_ca_topic_score_gemma":0.00003934537,"domain_scores_codex":[0.9980729,0.0005566894,0.0004487339,0.000303647,0.0002037711,0.0004142171],"domain_scores_gemma":[0.9958709,0.002933445,0.0002789635,0.0001928778,0.0003060155,0.0004178072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00004612837,0.000162892,0.2722837,0.0009169982,0.0000711519,0.000002884538,0.009896825,2.944388e-7,0.00003739948,0.4203351,0.1328627,0.1633839],"study_design_scores_gemma":[0.003421007,0.001123685,0.5075765,0.0002473474,0.000009112366,0.00007570349,0.01161328,0.0121594,0.00002671065,0.2848854,0.1776897,0.001172128],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02590703,0.0003172296,0.9675986,0.004137542,0.0003302066,0.0004628814,0.000584288,0.0001894518,0.0004727966],"genre_scores_gemma":[0.5723278,0.002350482,0.4192481,0.001846693,0.0002782323,0.0002119793,0.0001501099,0.00007962932,0.003506998],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5483505,"threshold_uncertainty_score":0.9984,"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."}}