{"id":"W3195400065","doi":"10.3390/info12090344","title":"VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification","year":2021,"lang":"en","type":"article","venue":"Information","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Interpretability; Visual analytics; Computer science; Naive Bayes classifier; Random forest; Machine learning; Analytics; Predictive analytics; Support vector machine; Interactive visual analysis; Artificial intelligence; Visualization; Data mining; Decision tree; Data science","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":[],"consensus_categories":[],"category_scores_codex":[0.00018807,0.00006566092,0.00007487311,0.000141929,0.00007081123,0.0004107751,0.0002140709,0.00004256517,0.000005326309],"category_scores_gemma":[0.0002122171,0.00006990483,0.00004065386,0.0005624918,0.000009536022,0.002983672,0.00007723198,0.00006042161,0.00004069048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007749528,"about_ca_system_score_gemma":0.0001384628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001280869,"about_ca_topic_score_gemma":0.00001057428,"domain_scores_codex":[0.9992903,0.00002147275,0.0002522234,0.0001135122,0.0001946247,0.0001278137],"domain_scores_gemma":[0.9993651,0.00003531358,0.0001132853,0.0002386268,0.0001845352,0.00006314162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002634512,0.000187016,0.00457069,0.0003334767,0.00002698067,0.000007683374,0.002437081,0.002941614,0.0002707421,0.916773,0.01198543,0.06043994],"study_design_scores_gemma":[0.0003694234,0.000008719166,0.02298376,0.00002767106,0.000007532343,0.000001272427,0.0001967919,0.9460474,0.0001424964,0.001662617,0.02845215,0.0001001666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003986485,0.00003080595,0.9939037,0.001395172,0.0001991509,0.0001158114,0.00001942012,0.00005968541,0.0002897597],"genre_scores_gemma":[0.9861799,0.00004958142,0.01084709,0.001272413,0.00005272234,0.00002170646,0.001402278,0.000004740205,0.0001696057],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9830566,"threshold_uncertainty_score":0.3961114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04556151753470707,"score_gpt":0.3394224635250304,"score_spread":0.2938609459903234,"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."}}