{"id":"W3127428087","doi":"10.1140/epjb/s10051-020-00017-3","title":"A bank liquidity multilayer network based on media emotion","year":2021,"lang":"en","type":"article","venue":"The European Physical Journal B","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Market liquidity; Business; Liquidity risk; Liquidity crisis; Stock (firearms); Financial system; Finance; Engineering","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":[0.0003492223,0.0003768426,0.0004377444,0.001455831,0.0004186905,0.0009971866,0.0006120551,0.0009018643,0.002635356],"category_scores_gemma":[0.002310617,0.000265268,0.0005291122,0.0009188442,0.0003620621,0.001469486,0.000657319,0.0006908407,0.000346361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006820416,"about_ca_system_score_gemma":0.0003123507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00671488,"about_ca_topic_score_gemma":0.006474284,"domain_scores_codex":[0.999822,0.000047032,0.000008641994,0.00005846376,0.00002831618,0.00003554749],"domain_scores_gemma":[0.9994024,0.0002831457,0.00008810757,0.00003617338,0.0001319103,0.00005820579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002304283,0.0005725505,0.03977102,0.0002870953,0.0004897333,0.001100932,0.0006050338,0.649972,0.02745398,0.04622408,0.01365014,0.2175691],"study_design_scores_gemma":[0.000009780944,0.00003601458,0.003042188,0.00001215212,0.0000533645,0.00004125329,0.00002656891,0.9873301,0.0008125266,0.008163779,0.0004583823,0.00001395785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.515186,0.002189663,0.4630574,0.003254478,0.0003507974,0.0000903476,0.002456253,0.001326147,0.01208892],"genre_scores_gemma":[0.9848046,0.0003199933,0.01184691,0.00008725183,0.00007629628,0.00003110953,0.000470654,0.00001559688,0.002347459],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00671488,"threshold_uncertainty_score":0.01335162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02936522942706134,"score_gpt":0.2294392248691763,"score_spread":0.200073995442115,"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."}}