{"id":"W3196569328","doi":"10.1016/j.irfa.2021.101882","title":"Multilayer financial networks and systemic importance: Evidence from China","year":2021,"lang":"en","type":"article","venue":"International Review of Financial Analysis","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Systemic risk; Linkage (software); Network structure; Financial contagion; Business; China; Diversity (politics); Financial networks; Financial crisis; Finance; Economics; Financial market; Computer science; Geography; Machine learning","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":[0.0008375228,0.0001694862,0.0002778651,0.001549686,0.0004138166,0.0008816457,0.0002213666,0.0002477185,0.001876388],"category_scores_gemma":[0.003658084,0.0001019572,0.0001946091,0.002550767,0.0006873168,0.0007898047,0.0006227104,0.0003283497,0.00009779847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007807673,"about_ca_system_score_gemma":0.0006996001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03724981,"about_ca_topic_score_gemma":0.04160697,"domain_scores_codex":[0.9998168,0.0000563102,0.0000137634,0.00003407283,0.00004210812,0.0000370589],"domain_scores_gemma":[0.9955909,0.001580901,0.001543944,0.0002359812,0.0007102817,0.0003379156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001407197,0.00004177924,0.9606546,0.00007124525,0.0002006415,0.0001737582,0.0006196533,0.001640481,0.0002624126,0.003460705,0.001054863,0.03167919],"study_design_scores_gemma":[0.00001483338,0.00003209635,0.9902132,0.00002757669,0.000134227,0.0000578945,0.0005282516,0.004472754,0.0001285119,0.003141992,0.001240165,0.000008590153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960036,0.001245639,0.0003991589,0.0005420532,0.000004930732,0.000005525523,0.0002163496,0.000004974439,0.00157778],"genre_scores_gemma":[0.9989304,0.0006531578,0.00007868153,0.00002281798,0.00001047003,0.000001869354,0.0001229108,7.980595e-7,0.0001788605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03724981,"threshold_uncertainty_score":0.07406598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01791921033572777,"score_gpt":0.259792080281667,"score_spread":0.2418728699459392,"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."}}