{"id":"W2901799605","doi":"10.6000/1929-7092.2018.07.57","title":"The Research on Stability of the Russian Banking System by Machine Learning Methods","year":2018,"lang":"en","type":"article","venue":"Journal of Reviews on Global Economics","topic":"Economic and Technological Developments in Russia","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stability (learning theory); Financial stability; Business; Financial system; Computer science; Machine learning","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":[0.02029382,0.0001069948,0.0004131108,0.00003269165,0.00102257,0.00008981177,0.001119779,0.0001225151,0.00007634176],"category_scores_gemma":[0.001618219,0.0000540183,0.0002050777,0.0002537368,0.0006740454,0.00008671738,0.0001380195,0.0005884837,0.00003300713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001124016,"about_ca_system_score_gemma":0.0002186529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008290115,"about_ca_topic_score_gemma":0.0001741757,"domain_scores_codex":[0.9964121,0.002029341,0.0009187725,0.0001648458,0.0001777215,0.0002972118],"domain_scores_gemma":[0.9977546,0.0006989164,0.001069536,0.0003035229,0.0000885634,0.00008480409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005437417,0.00004040062,0.009027835,0.00002380522,0.00004220012,3.379521e-7,0.0002968633,0.000006290367,0.000003896012,0.8140196,0.001713535,0.1747709],"study_design_scores_gemma":[0.000127901,0.0002010512,0.001606353,0.0002410206,0.000009363227,0.000002922335,0.0008299779,0.00007764089,0.0001821193,0.01244998,0.9841983,0.00007339833],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4793031,0.0144355,0.0005367306,0.01596794,0.003314827,0.001174915,0.00002840661,0.00003961695,0.485199],"genre_scores_gemma":[0.9837787,0.01285625,0.002783893,0.0001728709,0.0002058552,0.000004546034,2.010904e-7,0.000006349867,0.0001913357],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9824848,"threshold_uncertainty_score":0.7864884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1437473979660088,"score_gpt":0.4427070472093758,"score_spread":0.2989596492433669,"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."}}