{"id":"W4205675269","doi":"10.1016/j.adiac.2021.100581","title":"Effects of ASU 2016-13 and COVID-19 on banks' loan loss allowances","year":2022,"lang":"en","type":"article","venue":"Advances in Accounting","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Loan; Coronavirus disease 2019 (COVID-19); Accounting; Accrual; Business; Actuarial science; Finance; Earnings; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01064979,0.000619188,0.001739671,0.001129861,0.00149078,0.006960947,0.002525085,0.003641875,0.01144326],"category_scores_gemma":[0.03931488,0.0006424112,0.001687136,0.001258474,0.001757767,0.001900418,0.003207934,0.006116444,0.003442273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004554609,"about_ca_system_score_gemma":0.008441982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06154392,"about_ca_topic_score_gemma":0.08110575,"domain_scores_codex":[0.993579,0.001576119,0.0004872638,0.0007607158,0.001664985,0.001931881],"domain_scores_gemma":[0.9397547,0.02497471,0.01559105,0.002260926,0.007169978,0.0102486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.04039859,0.006486951,0.6354553,0.000417008,0.00117487,0.001250379,0.001455563,0.04357901,0.006971366,0.04443797,0.1591201,0.05925287],"study_design_scores_gemma":[0.00115017,0.002433854,0.9320275,0.0001117177,0.0004579005,0.0001757016,0.002489456,0.02213601,0.005666626,0.005236668,0.02790722,0.0002071349],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9686294,0.0007411706,0.0005060885,0.006049429,0.001063793,0.00006737192,0.00603078,0.0004998024,0.01641217],"genre_scores_gemma":[0.9776931,0.0001938113,0.0002538352,0.0009745284,0.0002958821,0.00009569793,0.00495269,0.0001145352,0.01542589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06154392,"threshold_uncertainty_score":0.1223714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01004532273675146,"score_gpt":0.2495270823030911,"score_spread":0.2394817595663397,"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."}}