{"id":"W2042139369","doi":"10.1080/03610926.2010.533230","title":"A Bayesian Meta-Modeling Approach for Gaussian Stochastic Process Models Using a Non Informative Prior","year":2012,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Constructive; Bayesian probability; Artificial intelligence; Machine learning; Gaussian process; Gaussian; Process (computing)","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":[0.007956471,0.001734849,0.002632018,0.00274623,0.001066683,0.002301919,0.003746758,0.00234796,0.002745723],"category_scores_gemma":[0.01070296,0.001636948,0.003200164,0.002202053,0.000915671,0.002840393,0.002126554,0.002767022,0.0006086436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00218789,"about_ca_system_score_gemma":0.002604903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007700226,"about_ca_topic_score_gemma":0.008720362,"domain_scores_codex":[0.9962573,0.002373768,0.0001492547,0.0004677762,0.0005949199,0.0001569618],"domain_scores_gemma":[0.9940901,0.004389572,0.0004608213,0.0003543596,0.000566515,0.000138517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004719198,0.00006267562,0.0005872131,0.00009021502,0.0002100373,0.0000565437,0.00007926205,0.9307159,0.0006617511,0.0339096,0.0004397816,0.03313984],"study_design_scores_gemma":[0.000006480392,0.00001167394,0.00005366031,0.000009802697,0.00002351302,0.00001015958,0.000005430938,0.9904757,0.0001311231,0.009003928,0.0002595941,0.000008891729],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001601353,0.0001263317,0.9978293,0.0000706368,0.000008098514,0.00002322642,0.00002398755,0.00008521887,0.0002317316],"genre_scores_gemma":[0.1949787,0.000520893,0.8017652,0.0001475609,0.00007176811,0.0005534405,0.0002956823,0.0001479488,0.001518793],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007956471,"threshold_uncertainty_score":0.04207838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1064730569018718,"score_gpt":0.4337829574225255,"score_spread":0.3273099005206537,"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."}}