{"id":"W3155832950","doi":"10.2139/ssrn.3046026","title":"Leverage - A Broader View","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Business; Medicine; Computer science; Artificial intelligence","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":[0.002009039,0.0009859712,0.001257955,0.004970756,0.001829982,0.01339141,0.001698643,0.007469267,0.01010492],"category_scores_gemma":[0.005615996,0.0004363787,0.0006876342,0.003690969,0.01637981,0.02039786,0.004568794,0.009600661,0.001253291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002144587,"about_ca_system_score_gemma":0.001355767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002447054,"about_ca_topic_score_gemma":0.001929915,"domain_scores_codex":[0.9976512,0.0006396109,0.0001089275,0.0006903488,0.0006558395,0.0002540938],"domain_scores_gemma":[0.9968423,0.001408556,0.0004004131,0.0004757228,0.0004848163,0.0003881818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000007685363,0.00001242907,0.0002846559,0.00002701131,0.000008421985,0.00004012903,0.0002208533,0.0001450357,0.00006032355,0.9912881,0.002028142,0.00587721],"study_design_scores_gemma":[0.00001321204,0.00002669181,0.001411314,0.0001745824,0.00002773289,0.0001629153,0.0003152738,0.0008394421,0.00009626876,0.9477501,0.04916264,0.00001983118],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04610133,0.07851872,0.05598648,0.1387041,0.002375016,0.00004624925,0.0004430684,0.0001273556,0.6776976],"genre_scores_gemma":[0.8946785,0.02980535,0.003109242,0.01535293,0.01557868,0.000107455,0.0001983774,0.0001117468,0.04105752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01339141,"threshold_uncertainty_score":0.03380436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02216334086696395,"score_gpt":0.2447816012929053,"score_spread":0.2226182604259414,"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."}}