{"id":"W7095886856","doi":"","title":"on our Web site: www.osfi-bsif.gc.caACTUARIAL STUDY NO. 6 OPTIMAL FUNDING OF THE CANADA PENSION PLAN OFFICE OF THE CHIEF ACTUARY TABLE OF CONTENTS","year":2007,"lang":"en","type":"article","venue":"","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Actuary; Plan (archaeology); Pension plan; Table (database); Pension","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009938754,0.0005100525,0.000835226,0.003039462,0.001073384,0.001657028,0.001469089,0.001438735,0.8046136],"category_scores_gemma":[0.006321921,0.0003552208,0.0005798236,0.003915248,0.0003194197,0.0007304522,0.0009387106,0.0009252932,0.4498216],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003914967,"about_ca_system_score_gemma":0.007263829,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.299674,"about_ca_topic_score_gemma":0.3766401,"domain_scores_codex":[0.9993074,0.00005111502,0.00004088168,0.00005341239,0.0003367735,0.0002104562],"domain_scores_gemma":[0.9949589,0.0008781908,0.0002087036,0.0003964795,0.001982375,0.001575302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004732367,0.00003036242,0.0006404791,0.00005766305,0.000001905954,0.000008152717,0.000006294045,0.00001521814,0.00002127001,0.0002309117,0.9868737,0.01206675],"study_design_scores_gemma":[0.0001739882,0.00004687538,0.0166428,0.0002886205,0.00001631452,0.00005210598,0.0000600974,0.0001075636,0.0002500322,0.0005454955,0.9817957,0.00002038247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.001637125,0.0008094671,0.0004420014,0.004525115,0.0009738742,0.0005762916,0.5781971,0.001413316,0.4114257],"genre_scores_gemma":[0.01169025,0.001782361,0.001973934,0.003086291,0.0009352664,0.0007760815,0.2749137,0.00165615,0.703186],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.996085,"threshold_uncertainty_score":0.5958593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02686759598220942,"score_gpt":0.2595738318023725,"score_spread":0.2327062358201631,"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."}}