{"id":"W3044336071","doi":"10.2139/ssrn.3572406","title":"What Do Analysts’ Provision Forecasts Tell Us About Expected Credit Loss Recognition?","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Business; Actuarial science; Economics; 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":[0.004960306,0.0003213722,0.0006490354,0.00187311,0.0003344868,0.004494532,0.0007174914,0.002464736,0.003536397],"category_scores_gemma":[0.07757077,0.0003338162,0.0004166847,0.001522495,0.0006409492,0.004642965,0.0004716549,0.002033994,0.001589338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009350507,"about_ca_system_score_gemma":0.0006513921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007263895,"about_ca_topic_score_gemma":0.006060531,"domain_scores_codex":[0.9985335,0.0003938468,0.0001480953,0.0003621414,0.0003729615,0.0001894466],"domain_scores_gemma":[0.9311387,0.04148155,0.0189294,0.001813108,0.005242297,0.001394905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008736028,0.0002227969,0.8857499,0.0001463366,0.0005449789,0.00028118,0.001175022,0.01695333,0.0006781882,0.008083605,0.02236898,0.06292204],"study_design_scores_gemma":[0.0001098843,0.0003445036,0.8633907,0.0002379609,0.0003489341,0.0004135229,0.003802794,0.0639886,0.002044696,0.05158285,0.01351578,0.000219726],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9342728,0.003131962,0.00822855,0.02266023,0.0005939034,0.00002924724,0.005110158,0.0001856714,0.02578728],"genre_scores_gemma":[0.9970877,0.0005329454,0.0003470212,0.0004064386,0.0002729096,0.00000613152,0.0007502047,0.00001890962,0.0005778297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007263895,"threshold_uncertainty_score":0.0262329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01261155573345451,"score_gpt":0.2247390248870357,"score_spread":0.2121274691535812,"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."}}