{"id":"W3082029294","doi":"10.1016/j.ejor.2020.08.048","title":"Inferring linear feasible regions using inverse optimization","year":2020,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Mathematical optimization; Feasible region; Set (abstract data type); Constraint (computer-aided design); Constrained optimization; Optimization problem; Function (biology); Algorithm; Mathematics","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":[0.001932044,0.00178008,0.002004757,0.002238429,0.0006763564,0.002885958,0.001669502,0.002331138,0.005343096],"category_scores_gemma":[0.01361886,0.002217395,0.002391318,0.001501417,0.001618308,0.003360855,0.002516057,0.002996474,0.001242643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007395237,"about_ca_system_score_gemma":0.001834995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005533509,"about_ca_topic_score_gemma":0.004780008,"domain_scores_codex":[0.998795,0.0004398136,0.00006672395,0.0002867855,0.0003159317,0.00009573052],"domain_scores_gemma":[0.9926561,0.006216813,0.0003124405,0.000324936,0.0004028829,0.00008679974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001524799,0.0001096474,0.0006494667,0.0002301067,0.00007672322,0.0001872258,0.00009759329,0.9198608,0.002698352,0.01330994,0.001250596,0.06137714],"study_design_scores_gemma":[0.00001351754,0.00002326944,0.00008358656,0.00001699018,0.00001257516,0.000025343,0.00001782428,0.9846107,0.0009117178,0.01378866,0.0004859933,0.000009824837],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007521039,0.0001309389,0.9902933,0.0001038745,0.00001453009,0.00003638758,0.00009626346,0.0003429054,0.001460846],"genre_scores_gemma":[0.3106529,0.0002993605,0.6850252,0.0001284813,0.00005229907,0.0002401973,0.0006069753,0.0003125425,0.002682016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005533509,"threshold_uncertainty_score":0.01787448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2361236133062016,"score_gpt":0.3985835627802454,"score_spread":0.1624599494740438,"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."}}