{"id":"W3120348770","doi":"10.2139/ssrn.3733932","title":"Bank Loan Markups and Adverse Selection","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Adverse selection; Loan; Competition (biology); Monetary economics; Market power; Shock (circulatory); Business; Markup language; Economics; Selection (genetic algorithm); Microeconomics; Finance","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":[0.004873875,0.0002162638,0.0005669935,0.001141644,0.0004958133,0.00302105,0.0003434071,0.00118678,0.008213686],"category_scores_gemma":[0.03281877,0.0002772271,0.000372314,0.001075164,0.001673483,0.001704821,0.0009091612,0.001372527,0.0007012018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004882658,"about_ca_system_score_gemma":0.0004390083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001134824,"about_ca_topic_score_gemma":0.0010784,"domain_scores_codex":[0.9984844,0.0008902967,0.0001141674,0.0001497895,0.0001495548,0.0002118956],"domain_scores_gemma":[0.9378198,0.04218216,0.01466443,0.001820927,0.001178924,0.002333821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00189631,0.0005769621,0.7485207,0.00012524,0.0003982996,0.001205225,0.001567877,0.02480811,0.001399982,0.1612842,0.006367095,0.05185005],"study_design_scores_gemma":[0.0001976772,0.0005786431,0.5838305,0.00007730926,0.0002879733,0.0009594181,0.001395806,0.05598295,0.0009722232,0.3519563,0.003667145,0.00009412292],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9800708,0.001345238,0.00559409,0.002596967,0.0000552563,0.00002191543,0.0001790079,0.00005870391,0.01007806],"genre_scores_gemma":[0.9980146,0.0002410336,0.00009686675,0.00006411795,0.00008666683,0.000004486028,0.00003189681,0.000003605835,0.001456765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008213686,"threshold_uncertainty_score":0.02747756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01133229894194095,"score_gpt":0.2027455458312572,"score_spread":0.1914132468893163,"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."}}