{"id":"W2133720322","doi":"10.1002/9780470012505.tad028","title":"Discretization of Distributions","year":2004,"lang":"en","type":"other","venue":"Encyclopedia of Actuarial Science","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Discretization; Simple (philosophy); Distribution (mathematics); Applied mathematics; Mathematics; Aggregate (composite); Computer science; Order (exchange); Discretization of continuous features; Discretization error; Algorithm; Mathematical optimization; Mathematical analysis; Materials science; Nanotechnology","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":[0.001210333,0.0005515103,0.0005942955,0.0005732264,0.0004270542,0.001717672,0.0009222782,0.0007775423,0.007105189],"category_scores_gemma":[0.005933257,0.0003240615,0.0008874408,0.0006123378,0.0009233332,0.00115126,0.001185743,0.001727924,0.001134502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001026427,"about_ca_system_score_gemma":0.0005973912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002643397,"about_ca_topic_score_gemma":0.001583695,"domain_scores_codex":[0.9989661,0.0004063945,0.00006800256,0.000153077,0.0003163977,0.00009003533],"domain_scores_gemma":[0.9980639,0.00115413,0.00008748927,0.0003965968,0.000238193,0.00005977082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005250025,0.00003209374,0.0006202313,0.00006359028,0.00001892565,0.00009187914,0.0001336994,0.6893846,0.002520749,0.2616234,0.00314314,0.0423152],"study_design_scores_gemma":[0.0000111818,0.000009610674,0.0001015411,0.00001842045,0.000002978126,0.00003856873,0.00002314392,0.9084982,0.0009638744,0.08288034,0.00744386,0.000008263984],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.004511327,0.0001248966,0.9874128,0.0001430709,0.00006010904,0.00003932439,0.0001757365,0.0002423363,0.007290424],"genre_scores_gemma":[0.4075215,0.0007759394,0.573666,0.0002632141,0.0001266759,0.0003262807,0.0007283695,0.0003130668,0.01627895],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.007105189,"threshold_uncertainty_score":0.02376926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03328963449778183,"score_gpt":0.3810483176375809,"score_spread":0.3477586831397991,"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."}}