{"id":"W4205790476","doi":"10.2139/ssrn.3973662","title":"CRM and AI in Time of Crisis","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Business; Computer science","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":[0.002067118,0.0001883851,0.0003404084,0.001487195,0.0004290242,0.003277333,0.0004972678,0.001437371,0.01471218],"category_scores_gemma":[0.0198313,0.0001201458,0.0001434042,0.001759301,0.0009486494,0.002935351,0.001151726,0.001807597,0.001390402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009573993,"about_ca_system_score_gemma":0.0007347542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005587326,"about_ca_topic_score_gemma":0.004344455,"domain_scores_codex":[0.9993166,0.0001854773,0.00005164685,0.0001162354,0.0001127403,0.0002173319],"domain_scores_gemma":[0.9882575,0.005017428,0.003501937,0.0002491416,0.001166393,0.001807649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002502143,0.0008013898,0.6949902,0.0003355953,0.0001704802,0.003521011,0.004910222,0.009477847,0.001757446,0.1440663,0.02909321,0.1083741],"study_design_scores_gemma":[0.0001031605,0.0005784449,0.8041263,0.0003118286,0.00009100461,0.002206434,0.01481767,0.03856706,0.000671098,0.1068774,0.03155134,0.00009846235],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9209592,0.00276071,0.002451107,0.01850882,0.000261781,0.00002561406,0.001005058,0.0001164596,0.05391115],"genre_scores_gemma":[0.997947,0.0002176812,0.0001387292,0.000144921,0.0001220586,0.000004179604,0.0001165704,0.00001172976,0.001297056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01471218,"threshold_uncertainty_score":0.04921716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007177442172889591,"score_gpt":0.2003513117717912,"score_spread":0.1931738695989016,"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."}}