{"id":"W4296693170","doi":"10.1016/j.frl.2022.103372","title":"Effect of COVID-19 on non-performing loans in China","year":2022,"lang":"en","type":"article","venue":"Finance research letters","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"Trent University; Concordia University","funders":"","keywords":"Non-performing loan; Coronavirus disease 2019 (COVID-19); Financial system; Loan; Business; China; Resilience (materials science); Financial crisis; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Monetary economics; Economics; Finance; Internal medicine; Medicine; Macroeconomics; Political science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001520624,0.0002771475,0.0005879774,0.001307406,0.001184804,0.00234811,0.0008642862,0.0007408716,0.005223735],"category_scores_gemma":[0.004298425,0.0001925515,0.0007157947,0.0013594,0.001159535,0.0007369898,0.001355413,0.0009581252,0.000380449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003199877,"about_ca_system_score_gemma":0.004354503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1420007,"about_ca_topic_score_gemma":0.1462489,"domain_scores_codex":[0.9985111,0.0002070631,0.0001491465,0.0002482587,0.0002442644,0.0006401562],"domain_scores_gemma":[0.9908723,0.001407414,0.003029859,0.0004718669,0.001673423,0.002545131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003674269,0.0001508332,0.9878656,0.00002601397,0.00008891548,0.0005559945,0.0005638183,0.001088687,0.0009384296,0.002198362,0.000951502,0.00520434],"study_design_scores_gemma":[0.00001395502,0.00006378013,0.9973132,0.000004039121,0.00003156536,0.00003771374,0.0004790117,0.001175529,0.0001750043,0.0002241274,0.0004736579,0.000008392994],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983932,0.0001110729,0.00004355596,0.0002243862,0.000006159758,0.000004661787,0.0001708201,0.0000121307,0.00103392],"genre_scores_gemma":[0.9985642,0.00003991364,0.000009222827,0.00002328244,0.00000771973,0.000002386893,0.0002476176,0.000002498102,0.001103162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1420007,"threshold_uncertainty_score":0.2823483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03313694135766619,"score_gpt":0.3140595655033253,"score_spread":0.2809226241456591,"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."}}