{"id":"W1918133462","doi":"10.1002/sim.4320","title":"Modeling continuous diagnostic test data using approximate Dirichlet process distributions","year":2011,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Victoria Hospital; McGill University Health Centre; Royal Victoria Regional Health Centre; McGill University","funders":"","keywords":"Nonparametric statistics; Dirichlet process; Computer science; Context (archaeology); Hierarchical Dirichlet process; Latent Dirichlet allocation; Parametric statistics; Identifiability; Cluster analysis; Bayesian probability; Data mining; Econometrics; Mathematics; Statistics; Machine learning; Artificial intelligence; Topic model","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":[0.0010354,0.0001829389,0.0003415034,0.0001108603,0.0001104334,0.00003029953,0.001261525,0.00006116108,0.00002277441],"category_scores_gemma":[0.005594708,0.0001507941,0.000009981279,0.0004541707,0.0001480224,0.0002740416,0.000349655,0.0002625215,0.000002654145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000379767,"about_ca_system_score_gemma":0.0001027738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002879171,"about_ca_topic_score_gemma":0.00004503204,"domain_scores_codex":[0.9982384,0.00009199551,0.0004618968,0.0005303505,0.0002936682,0.0003836465],"domain_scores_gemma":[0.9976237,0.0009650884,0.0001036992,0.001009962,0.0001628418,0.000134711],"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":[0.0000177478,0.0007065404,0.006016819,0.0003970542,0.0000499377,0.0008322574,0.00852656,0.0007649984,0.0002664446,0.8665887,0.002665029,0.113168],"study_design_scores_gemma":[0.0003310396,0.00005621851,0.0001439215,0.0001580212,0.00002875705,0.00002543827,0.00004660749,0.7843422,0.00002045582,0.2146779,0.00002393642,0.0001455431],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005451012,0.0003175704,0.9972911,0.0001447066,0.0003105568,0.0002393433,0.0004169933,0.00006645076,0.0006681511],"genre_scores_gemma":[0.2716959,0.00006953253,0.7278955,0.0001054929,0.00008449591,0.00001078927,0.0001147707,0.00001185461,0.00001171064],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7835772,"threshold_uncertainty_score":0.6697792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09951210076774852,"score_gpt":0.3634567137979474,"score_spread":0.2639446130301988,"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."}}