{"id":"W2126225382","doi":"10.5267/j.dsl.2014.9.005","title":"An application of data mining classification and bi-level programming for optimal credit allocation","year":2014,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Profitability index; Government (linguistics); Sustainable development; Computer science; Fuzzy logic; Genetic programming; Classifier (UML); Operations research; Business; Finance; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004504474,0.0000896449,0.0001718957,0.0003517051,0.0002412598,0.000160874,0.0008161291,0.00004861832,0.000003219793],"category_scores_gemma":[0.0009304847,0.00009908938,0.00002155586,0.0006752572,0.0003878417,0.001256237,0.00009422472,0.00004037163,0.000005193957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005830034,"about_ca_system_score_gemma":0.00002221635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002807919,"about_ca_topic_score_gemma":0.000007520873,"domain_scores_codex":[0.9982556,0.00001342219,0.0005659711,0.0008071727,0.0001501498,0.0002077299],"domain_scores_gemma":[0.9981027,0.0002525146,0.0004556824,0.001011859,0.0001151252,0.00006210873],"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":[0.00002129645,0.0001067735,0.09245897,0.00002484222,0.000003260172,2.065719e-8,0.000714901,0.001423266,0.02039125,0.05110611,0.0001185009,0.8336308],"study_design_scores_gemma":[0.0001970421,0.00004811266,0.3476385,0.000008062229,0.000002818194,8.039565e-7,0.00006201957,0.6469039,0.0001941936,0.002100783,0.002736186,0.0001075293],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4754454,0.000019205,0.5236673,0.0005218814,0.00007773776,0.0002145266,0.00002428982,0.00001357194,0.00001606879],"genre_scores_gemma":[0.7401305,0.000002252014,0.2596386,0.00009543733,0.00005086897,0.00002651936,0.00004697877,0.000007311966,0.000001421246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8335233,"threshold_uncertainty_score":0.4040748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1064726792399137,"score_gpt":0.32492018795872,"score_spread":0.2184475087188063,"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."}}