{"id":"W4288051725","doi":"10.1111/jmcb.12967","title":"Policy Uncertainty and Bank Mortgage Credit","year":2022,"lang":"en","type":"article","venue":"Journal of money credit and banking","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Bee Research Fund","keywords":"Loan; Mortgage loan; Identification (biology); Business; State (computer science); Financial system; Economics; Monetary economics; Finance; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001763781,0.0001169473,0.0003685554,0.0004351873,0.0003732426,0.002167479,0.0002705999,0.0006388184,0.002795735],"category_scores_gemma":[0.01808117,0.0001707143,0.0001649942,0.0007484048,0.0007089793,0.0008807484,0.0008557591,0.0009171828,0.0002047699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001194559,"about_ca_system_score_gemma":0.0005238086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00637548,"about_ca_topic_score_gemma":0.004876077,"domain_scores_codex":[0.9988968,0.0004242176,0.0000735803,0.000188374,0.0002473479,0.0001696543],"domain_scores_gemma":[0.9735522,0.01272755,0.01111657,0.0006645852,0.001175588,0.0007635171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004574454,0.0003538642,0.8616674,0.0001319359,0.0003424474,0.0002889004,0.001279414,0.06442017,0.002243347,0.04144723,0.003876608,0.02349119],"study_design_scores_gemma":[0.00005083128,0.0001819248,0.864371,0.00009350097,0.000116396,0.0001134193,0.00185932,0.06882008,0.0021674,0.05412223,0.008037905,0.00006602429],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870415,0.0009845113,0.002607266,0.002409575,0.00002332468,0.000009957941,0.0003738513,0.00002326825,0.006526724],"genre_scores_gemma":[0.9995522,0.00009585007,0.00006901873,0.00004545314,0.00001242872,0.000002450997,0.00003481791,0.000001397191,0.0001865147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00637548,"threshold_uncertainty_score":0.01267678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02164793506245585,"score_gpt":0.2385201247589828,"score_spread":0.2168721896965269,"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."}}