{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001876747,0.0001455382,0.0004142278,0.0005197483,0.0004489787,0.0001147622,0.0002289523,0.00005751614,0.0003522259],"category_scores_gemma":[0.0003111486,0.000156925,0.0001069592,0.0003696734,0.0001111584,0.0003018622,0.0001912264,0.0003865964,0.000002037262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001818111,"about_ca_system_score_gemma":0.00007548263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001614625,"about_ca_topic_score_gemma":0.000007033397,"domain_scores_codex":[0.9984657,0.00004682905,0.00081195,0.0002609856,0.0001479339,0.0002665697],"domain_scores_gemma":[0.9987109,0.0001329559,0.0008029403,0.0001892901,0.00006475033,0.00009918682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000247396,0.0005264471,0.6516002,0.0001798549,0.0002913396,0.0001018717,0.008605399,0.01285284,0.0004501283,0.2897627,0.007381302,0.02800048],"study_design_scores_gemma":[0.002289255,0.0007569153,0.5898533,0.00005462851,0.00005017411,0.0005816949,0.0006998342,0.01590043,0.00003982594,0.3264195,0.06274477,0.0006096296],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891552,0.00481207,0.0009021893,0.001898884,0.0008429903,0.0001016925,0.00006286531,0.00001566436,0.002208462],"genre_scores_gemma":[0.9979175,0.0002802155,0.0005142884,0.0001605512,0.0009617842,0.000003764085,0.000003835255,0.00001914862,0.0001389229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06174692,"threshold_uncertainty_score":0.6399216,"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."}}