{"id":"W3136434901","doi":"10.1017/s1748499521000087","title":"LRMoE.jl: a software package for insurance loss modelling using mixture of experts regression model","year":2021,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Censoring (clinical trials); Econometrics; Flexibility (engineering); Software; Truncation (statistics); Logistic regression; Data mining; Inflation (cosmology); R package; Statistics; Actuarial science; Mathematics; Economics; Machine learning","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":[0.0003333875,0.00009360606,0.0002112123,0.00004400606,0.0001892366,0.00002649965,0.0002245206,0.00005997415,0.00002416738],"category_scores_gemma":[0.002560753,0.00007956191,0.00007853981,0.0004304669,0.0002961929,0.0002091915,0.00005677532,0.00004733712,5.468876e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001552784,"about_ca_system_score_gemma":0.000283745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007785016,"about_ca_topic_score_gemma":0.000001040872,"domain_scores_codex":[0.998812,0.00001824746,0.0003477798,0.0002421489,0.0003760589,0.0002038275],"domain_scores_gemma":[0.9980163,0.0003821693,0.000238803,0.0003022063,0.000969887,0.00009066627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001048822,0.0004769629,0.00005369055,0.0002864529,0.00001690982,0.000002058467,0.001548602,0.03315696,0.1585712,0.7982299,0.002851546,0.004700891],"study_design_scores_gemma":[0.0001928542,0.00001577243,0.00004080098,0.0001408808,0.000008972997,0.000002178169,0.00006356408,0.4848414,0.2013727,0.3131835,0.00004446592,0.00009295499],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1633575,0.00002766922,0.835615,0.0003232721,0.0000472384,0.000148281,0.0003978876,0.00001773098,0.00006546183],"genre_scores_gemma":[0.7433088,0.00001290293,0.2565384,0.0000641227,0.00002036984,0.00001004023,0.00001509289,0.000005772152,0.00002455475],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5799513,"threshold_uncertainty_score":0.3244441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3112917869691673,"score_gpt":0.4611612129998646,"score_spread":0.1498694260306974,"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."}}