{"id":"W2068586653","doi":"10.1016/j.omega.2014.10.009","title":"Evaluation of bank branch growth potential using data envelopment analysis","year":2014,"lang":"en","type":"article","venue":"Omega","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":98,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Data envelopment analysis; Profitability index; Envelopment; Intermediation; Computer science; Focus (optics); Operations research; Industrial organization; Econometrics; Business; Economics; Engineering; Mathematics; Finance; Statistics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02939744,0.0001250522,0.0004351614,0.001098297,0.0001646986,0.0001635965,0.001530444,0.00006391337,0.0005803495],"category_scores_gemma":[0.006833723,0.00009869329,0.0001858359,0.004454665,0.0001072017,0.0004074701,0.0003754403,0.00007480734,0.00008821814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007371224,"about_ca_system_score_gemma":0.00020188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002076824,"about_ca_topic_score_gemma":0.0001215403,"domain_scores_codex":[0.9916614,0.00103567,0.0008434168,0.0007353435,0.005504853,0.0002193743],"domain_scores_gemma":[0.9955971,0.0004359983,0.000503834,0.001862142,0.001528264,0.00007260964],"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":[0.00004052056,0.0005312703,0.06888305,0.0000123366,0.002122083,0.000002585299,0.001066534,0.5055756,0.02988026,0.001537973,0.002642039,0.3877058],"study_design_scores_gemma":[0.0002591012,0.00001275757,0.03684535,0.000005261208,0.002139222,0.000001130882,0.00005059485,0.9531195,0.001170433,0.005698674,0.0005743481,0.0001236092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.716987,0.00008725704,0.2802003,0.0001724228,0.000196585,0.00008223875,0.00001633857,0.00001266801,0.002245204],"genre_scores_gemma":[0.9951978,0.000003351175,0.004530058,0.00005318362,0.00007011335,0.000001345003,0.00004074584,0.000006708011,0.00009673173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4475439,"threshold_uncertainty_score":0.9994396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2049934617388478,"score_gpt":0.4246943409870935,"score_spread":0.2197008792482457,"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."}}