{"id":"W4323718577","doi":"10.48550/arxiv.2303.04746","title":"Necessary and sufficient conditions for multiple objective optimal regression designs","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Optimal design; Mathematical optimization; Construct (python library); Computer science; Linear programming; Optimality criterion; Function (biology); Mathematics; Algorithm; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00220564,0.0004419497,0.000645068,0.0007413382,0.0005495749,0.0002817332,0.00117042,0.0004307568,0.0001074251],"category_scores_gemma":[0.002303701,0.0004100848,0.0003664322,0.0009622087,0.0004660122,0.0003741308,0.001856276,0.0004969658,0.0001403919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002571251,"about_ca_system_score_gemma":0.0002328111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001144289,"about_ca_topic_score_gemma":0.00002720113,"domain_scores_codex":[0.9959825,0.0007085412,0.000491359,0.001984457,0.0003536942,0.0004794756],"domain_scores_gemma":[0.992469,0.005160458,0.0005174621,0.001041723,0.0005009354,0.0003104218],"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":[0.001433982,0.000623885,0.01596186,0.0001098801,0.0003096786,0.0004556184,0.002960067,0.9176002,0.01203439,0.03337759,0.01410359,0.001029325],"study_design_scores_gemma":[0.001649079,0.000400559,0.01004867,0.0001998876,0.0001606641,0.000008790402,0.00841536,0.8797777,0.005861842,0.0921547,0.0004357654,0.0008870403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5215341,0.00009316858,0.4751267,0.00007717701,0.000844539,0.001076448,0.0004257841,0.0001836908,0.0006383052],"genre_scores_gemma":[0.9712011,0.00006816396,0.0227253,0.00005052971,0.00007834321,0.00001829356,0.00007502676,0.000051315,0.005731898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4524014,"threshold_uncertainty_score":0.9998351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4495448281470336,"score_gpt":0.3700793063775778,"score_spread":0.07946552176945582,"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."}}