{"id":"W2019672815","doi":"10.1111/poms.12205","title":"Bi‐level Programing Merger Evaluation and Application to Banking Operations","year":2014,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China; Organization Department of the Central Committee of the Communist Party of China; Canadian Imperial Bank of Commerce","keywords":"Incentive; Supply chain; Consolidation (business); Profit (economics); Industrial organization; Computer science; Operational efficiency; Business; Upstream (networking); Data envelopment analysis; Process management; Operations research; Operations management; Microeconomics; Marketing; Finance; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.005467835,0.000126148,0.0001440637,0.000574882,0.0009897907,0.0007723625,0.0001691179,0.00003099506,0.0000721704],"category_scores_gemma":[0.001488513,0.0001050196,0.00002822705,0.001302698,0.00006328592,0.0004830669,0.0001301029,0.00006341316,0.0001004164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004959528,"about_ca_system_score_gemma":0.00001702904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004774794,"about_ca_topic_score_gemma":0.0005992564,"domain_scores_codex":[0.9974084,0.0002565492,0.0004776682,0.0007830113,0.000915361,0.0001590029],"domain_scores_gemma":[0.9986596,0.00004708865,0.00005191724,0.0005458959,0.0006138465,0.00008167013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004624138,0.00009992906,0.001064785,0.000009025644,0.00002802423,8.76443e-8,0.001137679,0.3437353,0.002394041,0.03643816,0.0008654427,0.6142229],"study_design_scores_gemma":[0.0001837035,0.0000472034,0.02570147,0.00002063787,0.0001255185,0.000004047492,0.0009981712,0.9371634,0.0003989507,0.00193558,0.0331896,0.0002316904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2506087,0.0001164706,0.7281865,0.01640388,0.0004101037,0.001803505,0.000002199511,0.00008267275,0.002385959],"genre_scores_gemma":[0.9662296,0.00002463615,0.03072817,0.0003972869,0.0001397939,0.0003805471,0.00001693261,0.000008960261,0.002074037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7156209,"threshold_uncertainty_score":0.761277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08219682266193558,"score_gpt":0.3935581729004083,"score_spread":0.3113613502384728,"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."}}