{"id":"W2088225495","doi":"10.1057/palgrave.jors.2602619","title":"Assessing the performance of Canadian bank branches using data envelopment analysis","year":2008,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Data envelopment analysis; Inefficiency; Computer science; Order (exchange); Operations research; Information system; Project management; Econometrics; Scheduling (production processes); Economics; Business; Operations management; Statistics; Finance; Mathematics; Engineering; Microeconomics; Management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002212765,0.0006544199,0.0005323686,0.003146242,0.00179857,0.002861519,0.0006433814,0.0003833625,0.001321836],"category_scores_gemma":[0.007884312,0.0002370279,0.0005104783,0.006992099,0.0007653867,0.0007825337,0.0008663243,0.0003879507,0.0002497882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02729507,"about_ca_system_score_gemma":0.01873254,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9187889,"about_ca_topic_score_gemma":0.9198858,"domain_scores_codex":[0.9978269,0.0002366192,0.00007373584,0.0001548382,0.001227372,0.000480538],"domain_scores_gemma":[0.9964471,0.0007840248,0.0003301962,0.0001617365,0.002032294,0.0002446426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004304368,0.0001026663,0.4813454,0.0001869719,0.0001985312,0.0003674264,0.002028783,0.3361306,0.005418575,0.01185183,0.003313288,0.1586255],"study_design_scores_gemma":[0.00003083616,0.0001539607,0.6292067,0.0001165831,0.0001318132,0.00007452598,0.00380932,0.3463262,0.007009296,0.002350636,0.01063919,0.0001510226],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881631,0.0003319223,0.003381496,0.0001600133,0.000003875619,0.00004655179,0.000971015,0.00003414348,0.006907969],"genre_scores_gemma":[0.9947152,0.0002309326,0.003380075,0.000011582,0.000001842295,0.00001485995,0.0005936936,0.000008602758,0.001043289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08121109,"threshold_uncertainty_score":0.1980405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.521389292434807,"score_gpt":0.5187625611757518,"score_spread":0.002626731259055215,"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."}}