{"id":"W4296709748","doi":"10.1111/poms.13877","title":"Constrained optimization of objective functions determined from random forests","year":2022,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Mathematical optimization; Heuristics; Computer science; Random forest; Set (abstract data type); Tree (set theory); Sensitivity (control systems); Sampling (signal processing); Optimization problem; Mathematics; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009061259,0.0000996428,0.0001686163,0.0004017536,0.0007288782,0.0001180491,0.0001450806,0.00001929661,0.001010999],"category_scores_gemma":[0.0003575442,0.00008911686,0.00005041857,0.0008352943,0.00007895258,0.0003599162,0.0001360484,0.00007131742,0.000009493553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003747988,"about_ca_system_score_gemma":0.00003223527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006910694,"about_ca_topic_score_gemma":0.0001468375,"domain_scores_codex":[0.9981822,0.0002268654,0.0005032138,0.0004334481,0.0005544084,0.00009991212],"domain_scores_gemma":[0.9991288,0.00007993432,0.0001480779,0.0003444134,0.000255496,0.00004327897],"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":[0.00009228692,0.00008910678,0.002640672,0.000001430273,0.0000374862,8.862774e-7,0.0005740678,0.9750404,0.00004630566,0.001172056,0.002057424,0.01824789],"study_design_scores_gemma":[0.002493099,0.0002346767,0.01885618,0.00001111012,0.0001888463,0.00001856271,0.01109534,0.9442419,0.0004422329,0.003535455,0.01852738,0.0003552061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1221832,0.0001821763,0.8647458,0.002278134,0.001658278,0.001388798,0.0001101387,0.00007741053,0.007376051],"genre_scores_gemma":[0.9788992,0.0001834562,0.01348795,0.00007316643,0.00007607389,0.0002146233,0.0001467596,0.000008489907,0.006910277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.856716,"threshold_uncertainty_score":0.9999022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03030334422869278,"score_gpt":0.2981464081416849,"score_spread":0.2678430639129921,"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."}}