{"id":"W2941373075","doi":"10.1016/j.csda.2019.03.007","title":"Data-driven multistratum designs with the generalized Bayesian <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\" overflow=\"scroll\" id=\"d1e3469\" altimg=\"si339.gif\"><mml:mi>D</mml:mi></mml:math>-<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\" overflow=\"scroll\" id=\"d1e3474\" altimg=\"si339.gif\"><mml:mi>D</mml:mi></mml:math> criterion for highly uncertain models","year":2019,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Ministry of Science and Technology, Taiwan","keywords":"Bayesian probability; Bayesian information criterion; Optimal design; Computer science; Algorithm; Class (philosophy); Mathematics; Plot (graphics); Data mining; Artificial intelligence; Statistics; Machine learning","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":[0.05458308,0.002191621,0.003085921,0.002240964,0.001086362,0.002554528,0.003614943,0.002930869,0.01091576],"category_scores_gemma":[0.09471318,0.001938496,0.0028887,0.00161676,0.002431057,0.00288236,0.003391053,0.004241241,0.002012252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002768803,"about_ca_system_score_gemma":0.008270135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002232768,"about_ca_topic_score_gemma":0.002969106,"domain_scores_codex":[0.9619913,0.03002905,0.001107209,0.002740314,0.003528191,0.0006039304],"domain_scores_gemma":[0.9353654,0.05283574,0.002643936,0.004566406,0.003744397,0.0008439397],"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":[0.002559299,0.0003710753,0.001650415,0.001241517,0.0006366165,0.000140275,0.0003160606,0.3570639,0.002226323,0.4268158,0.00489224,0.2020865],"study_design_scores_gemma":[0.0006485787,0.001154594,0.0007802679,0.0002459154,0.0001951922,0.00005762217,0.00005286129,0.7350083,0.003089493,0.2514372,0.007233573,0.00009638842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002285554,0.0001363658,0.9957353,0.0002041497,0.00003437783,0.000456021,0.0001732424,0.0001573293,0.0008176595],"genre_scores_gemma":[0.08624896,0.0002594191,0.906716,0.0003415495,0.00006598694,0.003189285,0.0006460116,0.0001241761,0.002408509],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05458308,"threshold_uncertainty_score":0.2886664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06620302271394407,"score_gpt":0.3379909524056932,"score_spread":0.2717879296917491,"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."}}