{"id":"W3048504327","doi":"10.1002/aic.17021","title":"Using prior parameter knowledge in <scp>model‐based</scp> design of experiments for pharmaceutical production","year":2020,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Eli Lilly and Company","keywords":"Fisher information; Computation; Computer science; Bayesian probability; Selection (genetic algorithm); Bayesian information criterion; Invertible matrix; Prior information; Sequential analysis; Design of experiments; Algorithm; Process (computing); Mathematical optimization; Model selection; Matrix (chemical analysis); Mathematics; Machine learning; Artificial intelligence; 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":[0.03028277,0.001513515,0.00205016,0.001293736,0.0004272914,0.001376846,0.001207385,0.001163476,0.002041954],"category_scores_gemma":[0.04576836,0.001250052,0.001491988,0.0006837313,0.001276903,0.001604259,0.00124378,0.001832455,0.0002829903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001398987,"about_ca_system_score_gemma":0.003377699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001357107,"about_ca_topic_score_gemma":0.001935065,"domain_scores_codex":[0.9806465,0.01536687,0.0004887095,0.001002387,0.002251384,0.0002440844],"domain_scores_gemma":[0.9604024,0.03190394,0.002737997,0.002843724,0.001794594,0.0003173681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001789834,0.0009836027,0.002888325,0.001006587,0.0006900163,0.00008035055,0.0001223658,0.7511785,0.02253835,0.02461586,0.0008663086,0.1932399],"study_design_scores_gemma":[0.0003143745,0.001825422,0.002150603,0.000107573,0.000227176,0.00003820409,0.00002196401,0.9401602,0.01425299,0.0383513,0.00248855,0.00006163814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01106912,0.0001564087,0.9877414,0.0000913249,0.00001212717,0.0002558177,0.00004870364,0.00015411,0.000471005],"genre_scores_gemma":[0.3910972,0.0002357433,0.606178,0.0002081346,0.00002527321,0.001618928,0.0001801759,0.00006497215,0.0003916428],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03028277,"threshold_uncertainty_score":0.1601526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6399688166531778,"score_gpt":0.5589323547005753,"score_spread":0.0810364619526025,"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."}}