{"id":"W4400993448","doi":"10.69554/bnve6611","title":"Machine learning and its impact on financial institutions","year":2019,"lang":"en","type":"article","venue":"Journal of risk management in financial institutions","topic":"Business and Economic Development","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Business; Financial system; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004532507,0.00015559,0.0002341743,0.0002377722,0.000248624,0.00004342359,0.000171446,0.00006163595,0.0006128945],"category_scores_gemma":[0.0001225123,0.0001294081,0.00008590257,0.0003576594,0.00007363268,0.0005094102,0.0001704476,0.0003847282,0.0003587372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004266533,"about_ca_system_score_gemma":0.0001010218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001646777,"about_ca_topic_score_gemma":0.0002213506,"domain_scores_codex":[0.9989302,0.0000325935,0.0004273813,0.0001864922,0.0001845972,0.0002387348],"domain_scores_gemma":[0.9995185,0.00002539805,0.0002659094,0.00009248676,0.00001418208,0.00008351432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002145059,0.0004785858,0.4338225,0.0000282731,0.00004440035,0.0001471913,0.0003834126,0.4361911,0.00003634451,0.07374422,0.00133745,0.05357209],"study_design_scores_gemma":[0.001150077,0.000136542,0.7697079,0.00009641464,0.00003057849,0.00002646759,0.00003060275,0.001578839,0.00001077407,0.001303581,0.2257252,0.0002030927],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9650378,0.0001887217,0.0007039919,0.0001435535,0.0007492266,0.0002306029,0.00001443948,0.000009665575,0.03292201],"genre_scores_gemma":[0.9965913,0.002073549,0.000744433,0.0001127813,0.00005714911,0.000006509838,0.00000386563,0.000005933458,0.0004045033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4346122,"threshold_uncertainty_score":0.6710765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01603864064177372,"score_gpt":0.2565155267671274,"score_spread":0.2404768861253536,"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."}}