{"id":"W3195852475","doi":"10.2139/ssrn.3315741","title":"A Class of Mixture of Experts Models for General Insurance: Theoretical Developments","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Class (philosophy); Actuarial science; Economics; Econometrics; Business; Computer science; Artificial intelligence","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":[0.006163462,0.0001435616,0.0004463338,0.0001636348,0.00008403846,0.00003347701,0.0008333875,0.0001489852,0.00006025565],"category_scores_gemma":[0.0004354008,0.00009103895,0.0002614649,0.0002797942,0.0001874686,0.0003385688,0.00007579396,0.0005342385,0.000007143273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001982745,"about_ca_system_score_gemma":0.002210515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005656042,"about_ca_topic_score_gemma":0.00004126682,"domain_scores_codex":[0.9964555,0.0001881108,0.0008726196,0.0003041571,0.001021311,0.001158295],"domain_scores_gemma":[0.9981802,0.0004198462,0.0004002339,0.0003467342,0.0005721674,0.000080846],"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":[0.0003352779,0.0001047173,0.004231744,0.000008787198,0.00006833359,2.183738e-7,0.0005745921,0.002755812,0.001761681,0.9728151,0.0001059181,0.01723779],"study_design_scores_gemma":[0.0008992766,0.0002915417,0.0003729499,0.00002213409,0.000007628755,0.00003467725,0.0005568436,0.01416467,0.002003991,0.9812358,0.0002979279,0.0001125737],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.856807,0.001081975,0.1402797,0.0003262999,0.0002280014,0.0002651254,0.00001371817,0.00000548335,0.0009927175],"genre_scores_gemma":[0.995366,0.0004769218,0.00316457,0.00006732351,0.00006008262,0.000007154194,0.000001297263,0.00001186436,0.0008447844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.138559,"threshold_uncertainty_score":0.3921358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03306302590071317,"score_gpt":0.3270947088674562,"score_spread":0.294031682966743,"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."}}