{"id":"W4414441757","doi":"10.1016/j.orp.2025.100355","title":"Development of a robust design optimization algorithm for hierarchical time series pharmaceutical problems","year":2025,"lang":"en","type":"article","venue":"Operations Research Perspectives","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"National Research Foundation of Korea","keywords":"Quality by Design; Quality (philosophy); Optimization problem; Optimization algorithm; Hierarchical database model; Design of experiments; Optimal design; Development (topology)","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":[0.004921943,0.001582343,0.001641317,0.001096149,0.0004600528,0.001142435,0.001549164,0.001944899,0.003227764],"category_scores_gemma":[0.007903806,0.0008259062,0.001665489,0.0008686093,0.0008915599,0.001139218,0.001498858,0.002430797,0.0006595593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001070081,"about_ca_system_score_gemma":0.00260891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003472675,"about_ca_topic_score_gemma":0.002330645,"domain_scores_codex":[0.9981845,0.0007661681,0.0001285182,0.0004091408,0.0004044189,0.0001071373],"domain_scores_gemma":[0.995955,0.00293075,0.0003586183,0.0001638462,0.0005231435,0.00006870558],"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":[0.00004807536,0.00003447278,0.0001624892,0.00009336464,0.00003778199,0.00002331604,0.00002233189,0.9525189,0.001305291,0.01115779,0.0003700734,0.03422621],"study_design_scores_gemma":[0.000007131767,0.00002212096,0.00001939274,0.000004046107,0.000004150793,0.000004206816,0.000002278241,0.9975966,0.0003283801,0.001727529,0.0002812108,0.000002956212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005583893,0.00003510531,0.9990315,0.00003047179,0.000005916997,0.00002239201,0.000008713767,0.00005858782,0.000248849],"genre_scores_gemma":[0.0855904,0.0002144324,0.9121881,0.00009869746,0.00004099169,0.0005532045,0.000119049,0.00007482979,0.001120348],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004921943,"threshold_uncertainty_score":0.02603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3568773472423729,"score_gpt":0.5492687362278529,"score_spread":0.19239138898548,"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."}}