{"id":"W2592561635","doi":"10.1007/s00362-017-0887-7","title":"Using SeDuMi to find various optimal designs for regression models","year":2017,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"National Institute of General Medical Sciences","keywords":"Mathematical optimization; Optimal design; Semidefinite programming; Mathematics; Estimator; Discretization; Computer science; Focus (optics); Linear programming; Nonlinear programming; Equivalence (formal languages); Nonlinear system; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002310449,0.0002432711,0.0004280779,0.0001198251,0.0009815631,0.0007651515,0.001095511,0.0001247175,0.0006355635],"category_scores_gemma":[0.009216588,0.0001781341,0.0001023827,0.0001147287,0.0003041787,0.0004144691,0.0003072372,0.0001415362,0.0001431783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001087618,"about_ca_system_score_gemma":0.0001438834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009409669,"about_ca_topic_score_gemma":0.000005593508,"domain_scores_codex":[0.9967949,0.0002819761,0.0005370952,0.0008003489,0.001047386,0.0005382461],"domain_scores_gemma":[0.995019,0.003078907,0.0002178339,0.0009971384,0.0002096251,0.0004774977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001708627,0.000235485,0.0002327643,0.00002154107,0.00006208587,0.0001172577,0.001703259,0.04910829,0.5227506,0.1848318,0.01617969,0.2230486],"study_design_scores_gemma":[0.00161339,0.001030873,0.003343775,0.00008158314,0.00006889814,0.00002693607,0.0009970346,0.7808322,0.01129725,0.1942298,0.005571241,0.0009070693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01037971,0.00002523662,0.9729118,0.0002726826,0.0004931853,0.0005869574,0.0002565423,0.00002997342,0.01504393],"genre_scores_gemma":[0.3439495,8.376957e-7,0.6544212,0.0002342695,0.00006157003,0.00002709694,0.000004006954,0.00002330102,0.001278276],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7317238,"threshold_uncertainty_score":0.9991292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4964396165725281,"score_gpt":0.5570098859689532,"score_spread":0.06057026939642507,"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."}}