{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003973866,0.002219089,0.001317408,0.003451109,0.0006467723,0.002387558,0.003297455,0.001232814,0.1309004],"category_scores_gemma":[0.01326149,0.001141789,0.001623325,0.004424633,0.0004383856,0.002014616,0.003358942,0.002588388,0.06389996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001275027,"about_ca_system_score_gemma":0.002781854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01002744,"about_ca_topic_score_gemma":0.03502363,"domain_scores_codex":[0.9985452,0.0004583422,0.000188432,0.0002981255,0.0003765026,0.0001334828],"domain_scores_gemma":[0.9952109,0.002393157,0.0003045533,0.001244801,0.0006078388,0.0002388334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008466678,0.00003056763,0.001268559,0.0006285202,0.00006408134,0.00003317509,0.00005888571,0.0009184477,0.0001698518,0.002387511,0.9825305,0.01182519],"study_design_scores_gemma":[0.000638704,0.00002565916,0.004129525,0.0003990638,0.00006609249,0.0001727186,0.00008412721,0.009179667,0.001461782,0.0232178,0.960546,0.00007893273],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007001716,0.0002148172,0.01256666,0.0003044738,0.00009134886,0.00009702729,0.9642606,0.01826836,0.003496557],"genre_scores_gemma":[0.004407839,0.0002829185,0.03836979,0.0003059577,0.00004544395,0.001092804,0.9477605,0.004338718,0.003396181],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1309004,"threshold_uncertainty_score":0.4379054,"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."}}