{"id":"W4362504304","doi":"10.1007/978-3-030-54621-2_736-1","title":"Data-Driven Inverse Optimization","year":2022,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Inverse; Computer science; Mathematics; Geometry","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":[0.0002815094,0.0009189038,0.0009385295,0.0004207164,0.0002253218,0.001374793,0.0008946212,0.0008209007,0.01822221],"category_scores_gemma":[0.0008094758,0.0004964224,0.0005769674,0.0007279675,0.0005005975,0.0008558044,0.001414396,0.001420724,0.008470865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003082719,"about_ca_system_score_gemma":0.0004835321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007244094,"about_ca_topic_score_gemma":0.001273728,"domain_scores_codex":[0.9997732,0.00003180599,0.000008247183,0.00004150658,0.0001351604,0.00001014589],"domain_scores_gemma":[0.9997579,0.0001061335,0.00001133749,0.00004877963,0.00006552821,0.0000102603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009026362,0.00008390834,0.000211317,0.0005697116,0.00007711694,0.00009090868,0.00006247318,0.294116,0.01446238,0.1119841,0.08446427,0.4937876],"study_design_scores_gemma":[0.00001085977,0.00003076455,0.000186618,0.0000761939,0.00001406462,0.0001425436,0.00001804952,0.8283418,0.006308335,0.06655482,0.09829119,0.00002480992],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001114393,0.002529972,0.9485799,0.0002485067,0.0003708474,0.00002402616,0.0002681627,0.0009542448,0.04590997],"genre_scores_gemma":[0.1033559,0.006742944,0.7374628,0.0005181441,0.0005096926,0.0002672547,0.002287505,0.002106379,0.1467495],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01822221,"threshold_uncertainty_score":0.0609594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02618374193444844,"score_gpt":0.2597484799584814,"score_spread":0.2335647380240329,"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."}}