{"id":"W4226209088","doi":"10.5267/j.dsl.2022.4.003","title":"Determinants of credit risk: A multiple linear regression analysis of Peruvian municipal savings banks","year":2022,"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":"Solvency; Credit risk; Market liquidity; Variables; Economics; Sample (material); Inflation (cosmology); Interest rate; Econometrics; Regression analysis; Unemployment; Gross domestic product; Business; Actuarial science; Monetary economics; Statistics; Macroeconomics","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":[0.004431845,0.0001431234,0.0005927942,0.001992666,0.0005205409,0.00003740394,0.001218841,0.00004555898,0.0004632841],"category_scores_gemma":[0.002099848,0.0001455351,0.0002852992,0.005272252,0.00064407,0.0003359166,0.000506269,0.0001992624,0.00001053302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001906571,"about_ca_system_score_gemma":0.00003902523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007821245,"about_ca_topic_score_gemma":0.00008031209,"domain_scores_codex":[0.9972399,0.00005949423,0.001149066,0.0007338739,0.0004742072,0.0003434331],"domain_scores_gemma":[0.9970474,0.0005080125,0.001239818,0.001030798,0.00009539521,0.0000785697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003139477,0.000108028,0.9648436,0.000005207732,0.00001701297,0.000001555829,0.002282026,0.02468594,0.001739873,0.0002075337,0.00005536672,0.006022427],"study_design_scores_gemma":[0.0002392803,0.0000562314,0.7120529,0.000009928412,0.00002637156,0.000001101434,0.0001257582,0.285966,0.0004752907,0.0003833018,0.0005387962,0.0001250325],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942306,0.0001289123,0.004595458,0.0001330938,0.0004124587,0.0001679986,0.0002387991,0.00001530819,0.00007733872],"genre_scores_gemma":[0.9954153,0.00001087262,0.004393805,0.0001171122,0.00001806424,0.00001160486,0.000005900332,0.00001095304,0.00001641574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2612801,"threshold_uncertainty_score":0.5934747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03096855565313527,"score_gpt":0.2845859388464574,"score_spread":0.2536173831933222,"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."}}