{"id":"W3032832195","doi":"10.24149/gwp387","title":"Reserves and Risk: Evidence from China","year":2020,"lang":"en","type":"article","venue":"","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Ministry of Education, Culture, Sports, Science and Technology","keywords":"Credence; Business; Credit default swap; Private sector; Credit risk; Unintended consequences; China; Stock (firearms); Monetary economics; Financial system; Economics; Actuarial science; Financial economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001141004,0.0002619809,0.0003079917,0.001629597,0.0006580376,0.00080456,0.0003710469,0.0003991642,0.002462951],"category_scores_gemma":[0.002879242,0.0001568863,0.0004732262,0.003407729,0.001028146,0.0004359185,0.0008201216,0.0003896488,0.0001783177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009546453,"about_ca_system_score_gemma":0.001294539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1365674,"about_ca_topic_score_gemma":0.1461565,"domain_scores_codex":[0.9996667,0.00008199977,0.00003806005,0.00005994428,0.00007761628,0.00007578924],"domain_scores_gemma":[0.9942896,0.001545361,0.002646064,0.0004280088,0.0005531743,0.0005377821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009150732,0.00002995299,0.9939633,0.00004188484,0.0001309372,0.0004062019,0.0007377302,0.0003030351,0.0001550082,0.0005229056,0.0002595747,0.003357903],"study_design_scores_gemma":[0.00001347782,0.00003742944,0.997676,0.00001863092,0.00008190323,0.0000909652,0.000609039,0.0004024428,0.000117939,0.0002044522,0.0007396981,0.000008075877],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977909,0.0005026404,0.00004343348,0.0003222383,0.00000359623,0.000004259139,0.0002508288,0.000001410288,0.001080576],"genre_scores_gemma":[0.9988421,0.000447637,0.0000295363,0.00006452527,0.00001063863,0.000002577277,0.0002908461,7.967487e-7,0.0003113531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1365674,"threshold_uncertainty_score":0.2715449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06710079872597363,"score_gpt":0.2263710148454219,"score_spread":0.1592702161194482,"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."}}