{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009836799,0.00006792958,0.0002362577,0.0001500762,0.00004189587,0.00002904197,0.0001063251,0.00004482013,0.00006529608],"category_scores_gemma":[0.00003971934,0.00007959457,0.00005964187,0.0002120248,0.00001643493,0.0001347066,0.00003778703,0.0004895339,0.00006897719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001902849,"about_ca_system_score_gemma":0.0001604816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001110119,"about_ca_topic_score_gemma":0.0002767452,"domain_scores_codex":[0.9987484,0.00001061796,0.000370786,0.0001496805,0.00003275671,0.0006877534],"domain_scores_gemma":[0.9996873,0.000008939094,0.0001494443,0.0001037463,0.00002770229,0.00002292579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001320384,0.00007648937,0.08375344,0.000009865743,0.00004694927,0.000008312176,0.0002234173,0.00001423762,0.00003040586,0.9098235,0.0002306337,0.005769575],"study_design_scores_gemma":[0.0006538725,0.00009242306,0.0725091,0.00001333287,0.000005046137,0.00004117445,0.0004056619,0.000104216,0.00008315779,0.9155545,0.0104077,0.0001298361],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.966958,0.01992224,0.002762459,0.002087081,0.0001364088,0.00006079891,0.00000837229,0.000005428381,0.008059152],"genre_scores_gemma":[0.9836345,0.01477522,0.00004590611,0.0002779384,0.0000552235,0.000001677126,0.000001353671,0.000008842256,0.001199309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01667647,"threshold_uncertainty_score":0.3245772,"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."}}