{"id":"W2009242172","doi":"10.1111/j.0006-341x.2003.00124.x","title":"A Bayesian<i>A</i>‐Optimal and Model Robust Design Criterion","year":2003,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal General Hospital","funders":"","keywords":"Bayesian probability; TRACE (psycholinguistics); Optimal design; Set (abstract data type); Mathematical optimization; Mathematics; Limit (mathematics); Function (biology); Optimality criterion; Basis (linear algebra); Bayesian experimental design; Bayesian inference; Computer science; Applied mathematics; Statistics; Bayesian statistics","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":[0.01840749,0.001954222,0.002460268,0.001866291,0.0008488371,0.001909517,0.002171417,0.003585534,0.002696329],"category_scores_gemma":[0.03727772,0.001008043,0.00164518,0.001055043,0.003472677,0.002393996,0.002447512,0.002357383,0.0009300213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002111381,"about_ca_system_score_gemma":0.003965169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001257808,"about_ca_topic_score_gemma":0.0007213785,"domain_scores_codex":[0.9867977,0.007809052,0.0006262315,0.001790441,0.002523658,0.0004529778],"domain_scores_gemma":[0.9854066,0.01010771,0.001076404,0.001259888,0.001843156,0.0003062411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003128453,0.0002027235,0.001282028,0.0003925065,0.0002618287,0.0001044212,0.0001639753,0.3053755,0.007736282,0.6016287,0.003876387,0.07866276],"study_design_scores_gemma":[0.0001227901,0.000375,0.0006388353,0.0001110901,0.0000772878,0.0001076614,0.00003707195,0.5898151,0.003921876,0.3996413,0.005093773,0.00005827305],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002528016,0.0001061421,0.9948519,0.0002940559,0.00001518643,0.00006971556,0.00006967401,0.00006803393,0.001997402],"genre_scores_gemma":[0.1664155,0.0003187953,0.8290451,0.0008076608,0.0001046878,0.001197953,0.0002918376,0.0001226505,0.001695909],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01840749,"threshold_uncertainty_score":0.09734929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3170732228480225,"score_gpt":0.4331611706651947,"score_spread":0.1160879478171722,"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."}}