{"id":"W4399588815","doi":"10.32614/cran.package.shinystan","title":"shinystan: Interactive Visual and Numerical Diagnostics and Posterior Analysis for Bayesian Models","year":2015,"lang":"en","type":"dataset","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Bayesian probability; Interactive visual analysis; Computer science; Posterior probability; Visual analytics; Artificial intelligence; Visualization","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"],"consensus_categories":[],"category_scores_codex":[0.000247968,0.000293884,0.0005779651,0.0004117332,0.00008708232,0.0007639877,0.000561737,0.0001689029,0.00002672563],"category_scores_gemma":[0.0002565788,0.0002529231,0.00009036248,0.0005748525,0.00005994544,0.000695995,0.0007584455,0.0001479133,0.000005642446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004217934,"about_ca_system_score_gemma":0.0001032675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001160929,"about_ca_topic_score_gemma":0.0001098823,"domain_scores_codex":[0.9983462,0.00007210511,0.0003630697,0.0006676806,0.0003073841,0.0002435622],"domain_scores_gemma":[0.9983406,0.0004147289,0.0002001471,0.0004982804,0.0002413406,0.0003048767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001180506,0.0001014364,0.00002239757,0.00004325674,0.0003325763,0.000009874646,0.00009145735,0.00005317619,1.936686e-7,0.001054873,0.9971256,0.001153414],"study_design_scores_gemma":[0.0002599329,0.0002163862,0.00001721003,0.00002023738,0.0004838107,0.000009823459,0.00005259891,0.7145009,0.00000198509,0.0008174911,0.283299,0.0003206725],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.000003143812,0.00007094753,0.5401245,0.0001352402,0.0000652336,0.0001256141,0.4594342,0.00002493004,0.00001626219],"genre_scores_gemma":[0.0009018496,0.0003379966,0.01413866,0.001240681,0.0000861382,0.00002260734,0.9831049,0.00001472081,0.000152456],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.7144477,"threshold_uncertainty_score":0.9999923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02892416738287095,"score_gpt":0.3399790559290282,"score_spread":0.3110548885461572,"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."}}