{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006442044,0.000333255,0.0004400352,0.00153632,0.0008587271,0.004158972,0.0007335502,0.002103191,0.009777725],"category_scores_gemma":[0.04544209,0.0001484446,0.0002923841,0.002028194,0.002510681,0.004051562,0.001183233,0.002497208,0.0006358451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002881597,"about_ca_system_score_gemma":0.001806997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01045776,"about_ca_topic_score_gemma":0.008137257,"domain_scores_codex":[0.9978089,0.001512313,0.00005002085,0.0001075677,0.0003653514,0.0001557352],"domain_scores_gemma":[0.9200873,0.06816892,0.003566209,0.001374569,0.005035122,0.001767879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007832456,0.001158968,0.1345074,0.0003734329,0.0002350453,0.0003789112,0.0007424944,0.08846851,0.0006052545,0.4672669,0.03055754,0.2749223],"study_design_scores_gemma":[0.00006419146,0.0002017541,0.05549705,0.0003647259,0.00009169799,0.0001183843,0.001492621,0.1695161,0.0009568545,0.7360332,0.03559846,0.00006499038],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5593502,0.05218027,0.0124461,0.2145675,0.001265735,0.00003413729,0.0008698441,0.000215692,0.1590706],"genre_scores_gemma":[0.9866066,0.005818511,0.001106885,0.0007900792,0.0007140845,0.000007138663,0.00006560987,0.00001618295,0.00487494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01045776,"threshold_uncertainty_score":0.03406918,"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."}}